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#94 Psychometrics Models & Choosing Priors, with Jonathan Templin
Behavioral & Social Sciences Episode 9424th October 2023 • Learning Bayesian Statistics • Alexandre Andorra
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In this episode, Jonathan Templin, Professor of Psychological and Quantitative Foundations at the University of Iowa, shares insights into his journey in the world of psychometrics.

Jonathan’s research focuses on diagnostic classification models — psychometric models that seek to provide multiple reliable scores from educational and psychological assessments. He also studies Bayesian statistics, as applied in psychometrics, broadly. So, naturally, we discuss the significance of psychometrics in psychological sciences, and how Bayesian methods are helpful in this field.

We also talk about challenges in choosing appropriate prior distributions, best practices for model comparison, and how you can use the Multivariate Normal distribution to infer the correlations between the predictors of your linear regressions.

This is a deep-reaching conversation that concludes with the future of Bayesian statistics in psychological, educational, and social sciences — hope you’ll enjoy it!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca and Dante Gates.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

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Abstract

by Christoph Bamberg

You have probably unknowingly already been exposed to this episode’s topic - psychometric testing - when taking a test at school or university. Our guest, Professor Jonathan Templin, tries to increase the meaningfulness of these tests by improving the underlying psychometric models, the bayesian way of course!

Jonathan explains that it is not easy to judge the ability of a student based on exams since they have errors and are only a snapshot. Bayesian statistics helps by naturally propagating this uncertainty to the results.

In the field of psychometric testing, Marginal Maximum Likelihood is commonly used. This approach quickly becomes unfeasible though when trying to marginalise over multidimensional test scores. Luckily, Bayesian probabilistic sampling does not suffer from this.

A further reason to prefer Bayesian statistics is that it provides a lot of information in the posterior. Imagine taking a test that tells you what profession you should pursue at the end of high school. The field with the best fit is of course interesting, but the second best fit may be as well. The posterior distribution can provide this kind of information.

After becoming convinced that Bayes is the right choice for psychometrics, we also talk about practical challenges like choosing a prior for the covariance in a multivariate normal distribution, model selection procedures and more.

In the end we learn about a great Bayesian holiday destination, so make sure to listen till the end!


Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Transcripts

Speaker:

In this episode, Jonathan Templin,

professor of Psychological and

2

:

Quantitative Foundations at the University

of Iowa, shares insight into his journey

3

:

in the world of psychometrics.

4

:

Jonathan's research focuses on diagnostic

classification models, psychometric models

5

:

that seek to provide multiple reliable

scores from educational and psychological

6

:

assessment.

7

:

He also studies patient statistics as

applied in psychometrics, broadly.

8

:

So naturally, we discussed the

significance of psychometrics in

9

:

psychological sciences and how Bayesian

methods are helpful in this field.

10

:

We also talked about challenges in

choosing appropriate prior distributions,

11

:

best practices for model comparison, and

how you can use the multivariate normal

12

:

distribution to infer the correlations

between the predictors of your linear

13

:

progressions.

14

:

This is a deep, reaching conversation that

15

:

concludes with the future of Bayesian

statistics in Psychological, Educational,

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:

and Social Sciences.

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:

Hope you'll enjoy it.

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:

This is Learning Bayesian Statistics,

,:

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:

Hello, my dear Bayesians!

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This time, I have the pleasure to welcome

three new members to our Bayesian crew,

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Bart Trudeau, Noes Fonseca, and Dante

Gates.

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:

Thank you so much for your support, folks.

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:

It's the main way this podcast gets

funded.

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:

And Bart and Dante, get ready to receive

your exclusive merch in the coming month.

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:

Send me a picture, of course.

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:

Now let's talk psychometrics and modeling

with Jonathan Templin.

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Jonathan Templin, welcome to learning

patient statistics.

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Thank you for having me.

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It's a pleasure to be here.

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Yeah, thanks a lot.

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Quite a few patrons have mentioned you in

the Slack of the show.

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So I'm very honored to honor their request

and have you on the show.

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And actually thank you folks for bringing

me all of those suggestions and allowing

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me to discover so many good patients out

there in the world doing awesome things in

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a lot of different fields using our.

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favorite tools to all of us based in

statistics.

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So Jonathan, before talking about all of

those good things, let's dive into your

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origin story.

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How did you come to the world of

psychometrics and psychological sciences

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and how sinuous of a path was it?

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That's a good question.

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So I was an odd student, I dropped out of

high school.

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So I started my...

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college degree and community college, that

would be the only place that would take

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me.

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I happened to be really lucky to do that

though, because I had some really great

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professors and I took a, once I discovered

that I probably could do school, I took a

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statistics course, you know, typical

undergraduate basic statistics.

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I found that I loved it.

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I decided that I wanted to do something

with statistics and then in the process, I

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took a research methods class in

psychology and I decided somehow I wanted

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to do statistics in psychology.

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So moved on from community college, went

to my undergraduate for two years at

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Sacramento state and Sacramento,

California also was really lucky because I

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had professor there that said, Hey,

there's this field called quantitative

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psychology.

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You should look into it.

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If you're interested in statistics and

psychology along the same time, he was

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teaching me something called factor

analysis.

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I now look at it as more principal

components analysis, but I wanted to know

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what was happening underneath the hood of

factor analysis.

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And so that's where he said, no, really,

you should go to the graduate school for

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that.

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And so that's what started me.

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I was fortunate enough to be able to go to

the University of Illinois for graduate

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studies.

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I did a master's, a PhD there, and in the

process, that's where I learned all about

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Bates.

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So it was a really lucky route, but it all

wouldn't have happened if I didn't go to

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community college, so I'm really proud to

say I'm a community college graduate, if

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you will.

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Yeah.

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Nice.

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Yeah.

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So it kind of happened.

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somewhat easily in a way, right?

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Good meeting at the right time and boom.

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That's right.

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And the call of the eigenvalue is what

really sent me to graduate school.

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So I wanted to figure out what that was

about.

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Yes, that is a good point.

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And so nowadays,

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What are you doing?

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How would you define the work you're doing

and what are the topics that you are

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particularly interested in?

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I would put my work into the field of item

response theory, largely.

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I do a lot of multidimensional item

response theory.

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There are derivative fields I think I'm

probably most known for, one of which is

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something called cognitive diagnosis or

diagnostic classification modeling.

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Basically, it's a classification based

method to try to...

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Classify students, or I work in the

College of Education, so most of this is

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applied to educational data from

assessments, and our goal is to, whenever

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you take a test, not just give you one

score, give you multiple valid scores, try

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to maximize the information we can give

you.

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My particular focus these days is in doing

so in classroom-based assessments, so how

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do we understand what a student knows at a

given point in the academic year and try

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to help make sure that they make the most

progress they can.

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Not.

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to remove the impact of the teacher

actually to provide the teacher with the

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best data to work with the child, to work

with the parents, to try to move forward.

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But all that boils down to interesting

measurements, psychometric issues, and

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interesting ways that we look at test data

that come out of classrooms.

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Okay.

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Yeah, that sounds fascinating.

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Basically trying to give a distribution of

results instead of just one point

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estimate.

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That's it also and tests have a lot of

error.

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So making sure that we don't over deliver

when we have a test score.

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Basically understanding what that is and

accurately quantifying how much

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measurement error is or lack of

reliability there is in the score itself.

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Yeah, that's fascinating.

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I mean, we can already dive into that.

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I have a lot of questions for you, but it

sounds very interesting.

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So yeah.

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So what does it look like concretely?

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these measurement errors and the test

scores attached to them, and basically how

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do you try to solve that?

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Maybe you can take an example from your

work where you are trying to do that.

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Absolutely.

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Let me start with the classical example.

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If this is too much information, I

apologize.

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But to set the stage, for a long time in

item response theory, we understand that a

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person's...

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Latentability estimate, if you want to

call it that, is applied in education.

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So this latent variable that represents

what a person knows, it's put onto the

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continuum where items are.

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So basically items and people are sort of

ordered.

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However, the properties of the model are

such that how much error there might be in

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a person's point estimate of their score

depends on where the score is located on

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the continuum.

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So this is what, you know, theory gave

e to, you know, theory in the:

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gave rise to our modern computerized

adaptive assessments and so forth, that

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sort of pick an item that would minimize

the error, if you will, different ways of

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describing what we pick an item for.

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But that's basically the idea.

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And so from a perspective of where I'm at

with what I do, a complicating factor in

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this, so that architecture that I just

mentioned that

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historic version of adaptive assessments

that really been built on large scale

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measures.

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So thousands of students and really what

happens in a classical census you would

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take a marginal maximum likelihood

estimate of certain parameter values from

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the model.

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You'd fix those values as if you knew them

with certainty and then you would go and

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estimate a person's parameter value along

with their standard error conditional

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standard error measurement.

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The situations I work in don't have large

sample size but we all in addition to

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a problem with sort of the asthmatotic

convergence, if you will, of those models,

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we also have a, not only we have not have

large sample sizes, we also have multiple,

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multiple scores effectively, multiple

latent freqs that we can't possibly do.

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So when you look at the same problem from

a Bayesian lens, sort of an interesting

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feature happens that we don't often see,

you know,

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frequentness or a classical framework in

that process of fixing the parameters of

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the model, the item parameters to a value,

you know, disregards any error in the

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estimate as well.

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Whereas if you're in a simultaneous

estimate, for instance, in a markup chain

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where you're sampling these values from a

posterior in addition to sampling

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students, it turns out those that error

around those parameters can propagate to

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the students and provide a wider interval

around them, which I think is a bit more

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accurate, particularly in smaller sample

size.

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situation.

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So I hope that's the answer to your

question.

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I may have taken a path that might have

been a little different there, but that's

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where I see the value at least in using

Bayesian statistics and what I do.

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Yeah, no, I love it.

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Don't shy away from technical explanation

on these podcasts.

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That's the good thing of the podcast.

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Don't have to shy away from it.

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It came at a good time.

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I've been working on this, some problems

like this all day, so I'm probably in the

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weeds a little bit.

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Forgive me if I go at the deep end of it.

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No, that's great.

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And we already mentioned item response

theory on the show.

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So hopefully people will refer back to

these episodes and that will give them a

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heads up.

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Well, actually you mentioned it, but do

you remember how you first got introduced

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to Bayesian methods and why did they stick

with you?

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Very, very much.

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I was introduced because in graduate

school, I had the opportunity to work for

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a lab run by Bill Stout at the University

of Illinois with other very notable people

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in my career, at least Jeff Douglas, Louis

Roussos, among others.

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And I was hired as a graduate research

assistant.

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And my job was to take a program that was

a metropolis Hastings algorithm and to

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make it run.

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And it was written in Fortran.

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So basically, I

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It was Metropolis Hastings, Bayesian, and

it was written in language that I didn't

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know with methods I didn't know.

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And so I was hired and said, yeah, figure

it out with good luck.

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Thankfully, I had colleagues that could

help actually probably figure it out more

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than I did.

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But I was very fortunate to be there

because it's like a trial by fire.

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I was basically going line by line through

that.

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This was a little bit in the later part

of, I think it was the year:

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little early 2002.

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But something instrumental to me at the

time were a couple papers by a couple

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scholars in education at least, Rich Pates

d Brian Junker had a paper in:

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actually two papers in 1999, I can even,

you know, it's like Journal of Educational

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Behavioral Statistics.

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It's like I have that memorized.

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But in their algorithm, they had written

down the algorithm itself and it was a

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:

matter of translating that to the

diagnostic models that we were working on.

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But that is why it stuck with me because

it was my job, but then it was also

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incredibly interesting.

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It was not like a lot of the research that

I was reading and not like a lot of the

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work I was doing in a lot of the classes I

was in.

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So I found it really mentally stimulating,

entirely challenging.

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It took the whole of my brain to figure

out.

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And even then I don't know that I figured

it out.

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So that helps answer that question.

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Yeah.

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So basically it sounds like you were

thrown into the Beijing pool.

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Like you didn't have any choice.

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I was.

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When I was Bayesian, it was nice because

t the time, you know, this is:

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in education, no measurement in

psychology.

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You know, we knew of Bayes certainly, you

know, there's some great papers from the

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nineties that were around, but, you know,

we weren't, it wasn't prominent.

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It wasn't, you know, I was in graduate

school, but at the same time I wasn't

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learning it, I mean, I knew the textbook

Bayes, like the introductory Bayes, but

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not, definitely not.

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Like the estimation side.

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And so it was timing wise, you know,

people would look back now and say, okay,

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why didn't I go grab Stan or grab, at the

time I think we had, Jets didn't exist,

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there was bugs.

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And it was basically, you have to, you

know, like roll your own to do anything.

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So it was, it was good.

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No, for sure.

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Like, yeah, no, it's like telling, it's

like asking Christopher Columbus or

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That's right.

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It's a lot more direct.

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Just hop on the plane and...

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Wasn't an option.

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Exactly.

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Good point.

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But actually nowadays, what are you using?

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Are you still doing your own sampler like

that in Fortran or are you using some open

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source software?

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I can hopefully say I retired from Fortran

as much as possible.

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Most of what I do is install these days a

little bit of JAGS, but then occasionally

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I will...

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trying to write my own here or there.

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The latter part I'd love to do more of,

because you can get a little highly

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specialized.

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I just like that, I feel like the time to

really deeply do the development work in a

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way that doesn't just have an R package or

some package in Python that would just

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break all the time.

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So I'm sort of stuck right now with that,

but it is something that I'm grateful for

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having the contributions of others to be

able to rely upon to do estimation.

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Sorry.

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Yeah, no, exactly.

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I mean,

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So first, Stan, I've heard he's quite

good.

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Of course, it's amazing.

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A lot of Stan developers have been on this

show, and they do absolutely tremendous

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work.

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And yeah, as you were saying, why code

your own sampler when you can rely on

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samplers that are actually waterproof,

that are developed by a bunch of very

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smart people who do a lot of math.

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and who do all the heavy lifting for you,

well, just do that.

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And thanks to that, Bayesian computing and

statistics are much more accessible

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because you don't have to actually know

how to code your own MCMC sampler to do

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it.

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You can stand on the shoulders of giants

and just use that and superpower your own

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analysis.

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So it's definitely something we tell

people, don't code your own samplers now.

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You don't need to do that unless you

really, really have to do it.

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But usually, when you have to do that, you

know what you're doing.

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Otherwise, people have figured that out

for you.

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Just use the automatic samplers from Stan

or Pimsy or Numpyro or whatever you're

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using.

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It's usually extremely robust and checked

by a lot of different pairs of eyes and

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keyboards.

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having that team and like you said, full

of people who are experts in not only just

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mathematics, but also computer science

makes a big difference.

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Yeah.

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I mean, I would not be able to use patient

statistics nowadays if these samplers

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didn't exist, right?

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Because I'm not a mathematician.

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So if I had to write my own sample each

time, I would just be discouraged even

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before starting.

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Yeah.

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It's just a challenge in and of itself.

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I remember the old days where

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That would be it.

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That's my dissertation.

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That was what I had to do.

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So it was like six months work on just the

sampler.

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And even then it wasn't very good.

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And then they might actually do the

studying.

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Yeah, exactly.

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Yeah.

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I mean, to me really that probabilistic

programming is one of the super power of

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the Beijing community because that really

allows.

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almost anybody who can code in R or Python

or Julia to just use what's being done by

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very competent and smart people and for

free.

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Right.

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Yeah.

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Also true.

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Yeah.

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What a great community.

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I'm really, really impressed with the size

and the scope and how things have

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progressed in just 20 years.

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It's really something.

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Yeah.

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Exactly.

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And so actually...

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Do you know why, well, do you have an idea

why Bayesian statistics is useful in your

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field?

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What do they bring that you don't get with

the classical framework?

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Yeah, in particular, we have a really

nasty...

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If we were to do a classical framework,

typically the gold standard in...

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the field I work in is sort of a marginal

maximum likelihood.

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The marginal mean we get rid of the latent

variable to estimate models.

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So that process of marginalization is done

numerically.

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We numerically integrate across likelihood

function.

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Most cases, there are some special case

models that we really are too simplistic

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to use for what we do where we don't have

it.

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So if we want to do multidimensional

versions

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If you think about numeric integration,

for one dimension you have this sort of

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discretized set of a likelihood to take

sums across different, what we call

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quadrature points of some type of curve.

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For the multidimensional sense now, going

from one to two, you effectively squared

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the number of points you have.

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So that's just too latent variable.

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So if you want two bits of information

from an assessment from somebody, now

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you've just made your

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marginalization process exponentially more

difficult, more time-consuming.

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But really, the benefit of having two

scores is very little compared to having

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one.

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So if we wanted to do five or six or 300

scores, that marginalization process

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becomes really difficult.

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So from a brute force perspective, if we

take the a Bayesian sampler perspective,

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there is not the exponential increase of

computation in the linear increase in the

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latent variables.

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And so from a number of steps the process

has to take from calculation is much

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smaller.

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Now, of course, Markov chains have a lot

of calculations.

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So, you know, maybe overall the process is

longer, but it is, I found it to be

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necessity, basing statistics to estimate

in some form shows up in this

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multidimensional likelihood, basically

evaluation.

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:

created sort of hybrid versions of EM

algorithms where the E-step is replaced

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with the Bayesian type method.

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But for me, I like the full Bayesian

approach to everything.

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So I would say that just in summary

though, what Bayes brings from a brute

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force perspective is the ability to

estimate our models in a reasonable amount

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of time with a reasonable amount of

computations.

348

:

There's the added benefit of what I

mentioned previously, which

349

:

which is the small sample size, sort of

the, I think, a proper accounting or

350

:

allowing of error to propagate in the

right way if you're going to report scores

351

:

and so forth, I think that's an added

benefit.

352

:

But from a primary perspective, I'm here

because I have a really tough integral to

353

:

solve and Bayes helps me get around it.

354

:

Yeah, that's a good point.

355

:

And yeah, like as you were saying, I'm

guessing that having priors

356

:

And generative modeling helps for low

sample sizes, which tends to be the case a

357

:

lot in your field.

358

:

Also true.

359

:

Yeah.

360

:

The prior distributions can help.

361

:

A lot of the frustration with

multidimensional models and psychometrics,

362

:

at least in practical sense.

363

:

You get a set of data, you think it's

multidimensional.

364

:

The next process is to estimate a model.

365

:

in the classic sense that those models

sometimes would fail to converge.

366

:

Uh, and very little reason why, um,

oftentimes it's failed to emerge.

367

:

I had a class I taught four or five years

ago where I just asked people to estimate

368

:

five dimensions and not a single person

couldn't could get, I had a set of data

369

:

for each person.

370

:

Not a single person could get it in

marriage with the default options that

371

:

you'd see that like an IRT package.

372

:

Um, so having the ability to sort of.

373

:

Understand potentially where

non-convergence or why that's happening,

374

:

which parameters are finding a difficult

spot.

375

:

Then using priors to sort of aid an

estimation as one part, but then also sort

376

:

of the idea of the Bayesian updating.

377

:

If you're trying to understand what a

student knows throughout the year,

378

:

Bayesian updating is perfect for such

things.

379

:

You know, you can assess a student in

November and update their results that you

380

:

have potentially from previous parts in

the year as well, too.

381

:

So there's a lot of benefits.

382

:

I guess I could keep going.

383

:

I'm talking to a BASE podcast, so probably

I already know most of it.

384

:

Yeah.

385

:

I mean, a lot of people are also listening

to understand what BASE is all about and

386

:

how that could help them in their own

field.

387

:

So that's definitely useful if we have

some psychometricians in the audience who

388

:

haven't tried yet some BASE, well, I'm

guessing that would be useful for them.

389

:

And actually, could you share an example?

390

:

If you have one of a research project

where BASE and stats played a

391

:

a crucial role, ideally in uncovering

insights that might have been missed

392

:

otherwise, especially using traditional

stats approaches?

393

:

Yeah, I mean, just honestly, a lot of what

we do just estimating the model itself, it

394

:

sounds like it should be trivial.

395

:

But to do so with a full information

likelihood function is so difficult.

396

:

I would say almost every single analysis

I've done using a multidimensional

397

:

has been made possible because of the

Bayesian analyses themselves.

398

:

Again, there are shortcut methods you

would call that.

399

:

I think there are good methods, but again,

there are people, like I mentioned, that

400

:

sort of a hybrid marginal maximum

likelihood.

401

:

There's what we would call limited

information approaches that you might see

402

:

in programs like M plus, or there's an R

package named Laban that do such things.

403

:

But those only use functions of the data,

not the full data themselves.

404

:

I mean, it's still good, but it's sort of

I have this sense that the full likelihood

405

:

is what we should be using.

406

:

So to me, just a simple example, take a, I

was working this morning with a four

407

:

dimensional assessment, an assessment, you

know, 20 item test, kids in schools.

408

:

And you know, I would have a difficult

time trying to estimate that with a full

409

:

maximum likelihood method.

410

:

And so Bayes made that possible.

411

:

But beyond that, if we ever want to do

something with the test scores afterwards,

412

:

right?

413

:

So now we have a bunch of Markov chains of

people's scores themselves.

414

:

This makes it easy to be able to then not

forget that these scores are not measured

415

:

perfectly.

416

:

And take a posterior distribution and use

that in a secondary analysis as well, too.

417

:

So I was doing some work with one of the

Persian Gulf states where they were trying

418

:

to

419

:

like a vocational interest survey.

420

:

And some of the classical methods for

this, sort of they disregarded any error

421

:

whatsoever.

422

:

And they basically said, oh, you're

interested in, I don't know, artistic work

423

:

or you know, numeric work of some sort.

424

:

And they would just tell you, oh, that's

it.

425

:

That's your story.

426

:

Like, I don't know if you've ever taken

one of those.

427

:

What are you gonna do in a career?

428

:

You're in a high school student and you're

trying to figure this out.

429

:

But if you propagate, if you allow that

error to sort of propagate,

430

:

through the way Bayesian methods make it

very easy to do, you'll see that while

431

:

that may be the most likely choice of what

you're interested in or what your sort of

432

:

dimensions that may be most salient to you

in your interests, there are many other

433

:

choices that may even be close to that as

well.

434

:

And that would be informative as well too.

435

:

So we sort of forget, we sort of overstate

how certain we are in results.

436

:

And I think a lot of the Bayesian methods

built around it.

437

:

So

438

:

That was one actually project where I did

write the own algorithm for it to try to

439

:

estimate these things because it was just

a little more streamlined.

440

:

But it seemed it seemed that would rather

than telling a high school student, hey,

441

:

you're best at artistic things.

442

:

What we could say is, hey, yeah, you may

be best at artistic, but really close to

443

:

that is something that's numeric, you

know, like something along those lines.

444

:

So while you're strong at art.

445

:

You're really strong at math too.

446

:

Maybe you should consider one of these two

rather than just go down a path that may

447

:

or may not really reflect your interests.

448

:

Hope that's a good example.

449

:

Yeah.

450

:

Yeah, definitely.

451

:

Yeah, thanks.

452

:

And I understand how that would be useful

for sure.

453

:

And how does, I'm curious about the role

of priors in all that, because that's

454

:

often something that puzzles beginners.

455

:

And so you obviously have a lot of

experience in the Bayesian way of life in

456

:

your field.

457

:

So I'm curious, I'm guessing that you kind

of teach the way to do psychometric

458

:

analysis in the Bayesian framework to a

lot of people.

459

:

And I'm curious, especially on the prior

side, and if there are other interesting

460

:

things that you would like to share on

that, feel free.

461

:

My question is on the priors.

462

:

How do you approach the challenge of

choosing appropriate prior distributions,

463

:

especially when you're dealing with

complex models?

464

:

Great question.

465

:

And I'm sure each field does it a little

bit differently.

466

:

I mean, as it probably should, because

each field has its own data and models and

467

:

already established scientific knowledge.

468

:

So that's my way of saying.

469

:

This is my approach.

470

:

I'm 100% confident that it's the approach

that everybody should take.

471

:

But let me back it up a little bit.

472

:

So generally speaking, I teach a lot of

students who are going into, um, many of

473

:

our students end up in the industry for

educational measurement here in the United

474

:

States.

475

:

Um, I like, we usually denote our score

parameters with theta.

476

:

I like to go around saying that, yeah, I'm

teaching you to have to sell

477

:

That's sort of what they do, you know, in

a lot of these industry settings, they're

478

:

selling test scores.

479

:

So if you think that that's what you're

trying to do, I think that guides to me a

480

:

set of prior choices that try to do the

least amount of speculation.

481

:

So what I mean by that.

482

:

So if you look at a measurement model,

like an item response model, you know,

483

:

there's a set of parameters to it.

484

:

One parameter in particular, in item

response theory, we call it the

485

:

discrimination parameter or

486

:

Factor analysis, we call it factor

loading, and linear regression, it would

487

:

be a slope.

488

:

This parameter tends to govern the extent

to which an item relates to the latent

489

:

variable.

490

:

So the higher that parameter is, the more

that item relates.

491

:

Then when we go and do a Bayes theorem to

get a point estimate of a person's score

492

:

or a posterior distribution of that

person's score, the contribution of that

493

:

item.

494

:

is largely reflected by the magnitude of

that parameter.

495

:

The higher the parameter that is, the more

that item has weight on that distribution,

496

:

the more we think we know about a person.

497

:

So in doing that, when I look at setting

prior choices, what I try to do for that

498

:

is to set a prior that would be toward

zero, mainly, actually at zero mostly, try

499

:

to set it so that we want our data to tell

more of the job than our prior,

500

:

particularly if we're trying to, if this

score has a big,

501

:

uh, meaning to somebody you think of, um,

well, in the United States, the assessment

502

:

culture is a little bit out of control,

but, you know, we have to take tests to go

503

:

to college.

504

:

We have to take tests to go to graduate

school and so forth.

505

:

Uh, then of course, if you go and work in

certain industries, there's assessments to

506

:

do licensure, right?

507

:

So if you, you know, for instance, my

family is a, I come from that family of

508

:

nurses, uh, it's a very noble profession,

but to, to be licensed in a nurse in

509

:

California, you have to pass an exam.

510

:

provide that score for the exam that we're

not, that score reflects as much of the

511

:

data as possible unless a prior choice.

512

:

And so there are ways that, you know,

people can sort of use priors, they're

513

:

sort of not necessarily empirical science

benefit, you can sort of put too much

514

:

subjective weight onto it.

515

:

So when I talk about priors, when I talk

about the, I try to talk about the

516

:

ramifications of the choice of prior on

certain parameters, that discrimination

517

:

parameter or slope, I tend to want

518

:

to have the data to force it to be further

away from zero because then I'm being more

519

:

conservative, I feel like.

520

:

The rest of the parameters, I tend to not

use heavy priors on what I do.

521

:

I tend to use some very uninformative

priors unless I have to.

522

:

And then the most complicated prior for

what we do, and the one that's caused

523

:

historically the biggest challenge,

although it's, I think, relatively in good

524

:

place these days thanks to research and

science, is the prior that goes on a

525

:

covariance or correlation matrix.

526

:

That had been incredibly difficult to try

to estimate back in the day.

527

:

But now things are much, much easier in

modern computing, in modern ways of

528

:

looking, modern priors actually.

529

:

Yeah, interesting.

530

:

Would you like to walk us a bit through

that?

531

:

What are you using these days on priors on

correlation or covariance matrices?

532

:

Because, yeah, I do teach those also

because...

533

:

I love it.

534

:

Basically, if you're using, for instance,

a linear regression and want to estimate

535

:

not only the correlation of the

parameters, the predictors on the outcome,

536

:

but also the correlation between the

predictors themselves and then using that

537

:

additional information to make even better

prediction on the outcome, you would, for

538

:

instance, use a multivariate normal on the

parameters on your slopes.

539

:

of your linear regression, for instance,

what primaries do you use on that

540

:

multivariate?

541

:

What does the multivariate normal mean?

542

:

And a multivariate normal needs a

covariance matrix.

543

:

So what primaries do you use on the

covariance matrix?

544

:

So that's basically the context for

people.

545

:

Now, John, basically try and take it from

there.

546

:

What are you using in your field these

days?

547

:

Yeah, so going with your example, I have

no idea.

548

:

You know, like, if you have a set of

regression coefficients that you say are

549

:

multivariate normal, yes, there is a place

for a covariance in the prior.

550

:

I never try to speculate what that is.

551

:

I don't think I have, like, the human

judgment that it takes to figure out what

552

:

the, like, the belief, your prior belief

is for that.

553

:

I think you're talking about what would be

analogous to sort of the, like, the

554

:

asthmatotic covariance matrix.

555

:

The posterior distribution of these

parameters where you look at the

556

:

covariance between them is like the

asymptotic covariance matrix in ML, and we

557

:

just rarely ever speculate off of the

diagonal, it seems like, on that.

558

:

I mean, there are certainly uses for

linear combinations and whatnot, but

559

:

that's tough.

560

:

I'm more thinking about, like, when I have

a handful of latent variables and try to

561

:

estimate, now the problem is I need a

covariance matrix between them, and

562

:

they're likely to be highly correlated,

right?

563

:

So...

564

:

In our field, we tend to see correlations

of psychological variables that are 0.7,

565

:

0.8, 0.9.

566

:

These are all academic skills in my field

that are coming from the same brain.

567

:

The child has a lot of reasons why those

are going to be highly correlated.

568

:

And so these days, I love the LKJ prior

for it.

569

:

It makes it easy to put a prior on a

covariance matrix and then if you want to

570

:

rescale it.

571

:

That's one of the other weird features of

the psychometric world is that because

572

:

these variables don't exist, to estimate

covariance matrix, we'd have to make

573

:

certain constraints on the, on some of the

item parameters, the measurement model for

574

:

instance.

575

:

If we want a variance of the factor, we

have to set one of the parameters of the

576

:

discrimination parameters to a value to be

able to estimate it.

577

:

Otherwise, it's not identified.

578

:

work that we talk about for calibration

when we're trying to build scores or build

579

:

assessments and their data for it, we fix

that value of the variance of a factor to

580

:

one.

581

:

We standardize the factor zero, meaning

variance one, very simple idea.

582

:

The models are equivalent in a classic

sense, in that the likelihoods are

583

:

equivalent, whether we do one way or the

other.

584

:

When we put products on the posteriors

aren't entirely equivalent, but that's a

585

:

matter of a typical Bayesian issue with

transformations.

586

:

But

587

:

In the sense where we want a correlation

matrix, prior to the LKJ, prior, there

588

:

were all these sort of, one of my mentors,

Rod McDonald, called devices, little hacks

589

:

or tricks that we would do to sort of keep

covariance matrix, sample it, right?

590

:

I mean, you think about statistically to

sample it, I like a lot of rejection

591

:

sampling methods.

592

:

So if you were to basically propose a

covariance or correlation matrix, it has

593

:

to be positive.

594

:

semi-definite, that's a hard term.

595

:

It has to be, you have to make sure that

the correlation is bounded and so forth.

596

:

But LKJ takes care of almost all of that

for me in a way that allows me to just

597

:

model the straight correlation matrix,

which has really made life a lot easier

598

:

when it comes to estimation.

599

:

Yeah, I mean, I'm not surprised that does.

600

:

I mean, that is also the kind of priors I

tend to use personally and that I teach

601

:

also.

602

:

In this example, for instance, of the

linear regression, that's what I probably

603

:

end up using LKJPrior on the predictors on

the slopes of the linear regression.

604

:

And for people who don't know,

605

:

Never used LKJ prior.

606

:

LKJ is decomposition of the covariance

matrix.

607

:

That way, we can basically sample it.

608

:

Otherwise, it's extremely hard to sample

from a covariance matrix.

609

:

But the LKJ decomposition of the matrix is

a way to basically an algebraic trick.

610

:

that makes use of the Cholesky

decomposition of a covariance matrix that

611

:

allows us to sample the Cholesky

decomposition instead of the covariance

612

:

matrix fully, and that helps the sampling.

613

:

Thank you.

614

:

Thank you for putting that out there.

615

:

I'm glad you put that on.

616

:

Yeah, so yeah.

617

:

And basically, the way you would

parametrize that, for instance, in Poem C,

618

:

you would

619

:

use pm.lkj, and basically you would have

to parameterize that with at least three

620

:

parameters, the number of dimensions.

621

:

So for instance, if you have three

predictors, that would be n equals 3.

622

:

The standard deviation that you are

expecting on the predictors on the slopes

623

:

of the linear regression, so that's

something you're used to, right?

624

:

If you're using a normal prior on the

slope, then the sigma of the slope is just

625

:

standard deviation that you're expecting

on that effect for your data and model.

626

:

And then you have to specify a prior on

the correlation of these slopes.

627

:

And that's where you get into the

covariance part.

628

:

And so basically, you can specify a prior.

629

:

So that would be called eta in PIME-Z on

the LKJ prior.

630

:

And the

631

:

bigger eta, the more suspicious of high

correlations your prior would be.

632

:

So if eta equals 1, you're basically

expecting a uniform distribution of

633

:

correlations.

634

:

That could be minus 1, that could be 1,

that could be 0.

635

:

All of those have the same weight.

636

:

And then if you go to eta equals 8, for

instance, you would put much more prior

637

:

weight on correlations eta.

638

:

Close to zero, much of them will be close

to zero in 0.5 minus 0.5, but it would be

639

:

very suspicious of very big correlations,

which I guess would make a lot of sense,

640

:

for instance, social science.

641

:

I don't know in your field, but yeah.

642

:

I typically use the uniform, the one

setting, at least to start with, but yeah,

643

:

I think that's a great description.

644

:

Very good description.

645

:

Yeah, I really love these kinds of models

because they make linear regression even

646

:

more powerful.

647

:

To me, linear regression is so powerful

and very underrated.

648

:

You can go so far with plain linear

regression and often it's hard to really

649

:

do better.

650

:

You have to work a lot to do better than a

really good linear regression.

651

:

I completely agree with you.

652

:

Yeah, I'm 100% right there.

653

:

And actually then you get into sort of

the...

654

:

quadratic or the nonlinear forms in linear

regression that map onto it that make it

655

:

even more powerful.

656

:

So yeah, it's absolutely wonderful.

657

:

Yeah, yeah.

658

:

And I mean, as Spider-Man's uncle said,

great power comes with great

659

:

responsibility.

660

:

So you have to be very careful about the

priors when you have all those features,

661

:

so inversing functions because they

662

:

the parameter space, but same thing, well,

if you're using a multivariate normal, I

663

:

mean, that's more complex.

664

:

So of course you have to think a bit more

about your model structure, about your

665

:

prior.

666

:

And also the more structure you add, if

the size of the data is kept equal, well,

667

:

that means you have more risk for

overfitting and you have less informative

668

:

power per data point.

669

:

Let's say so.

670

:

That means the prior.

671

:

increase in importance, so you have to

think about them more.

672

:

But you get a much more powerful model

after once and the goal is to get much

673

:

more powerful predictions after once.

674

:

I do agree.

675

:

These weapons are hard to wield.

676

:

They require time and effort.

677

:

And on my end, I don't know for you.

678

:

Jonathan, but on my end, they also require

a lot of caffeine from time to time.

679

:

Maybe.

680

:

Yeah.

681

:

I mean, so that's the key.

682

:

You see how I did the segue.

683

:

I should have a podcast.

684

:

Yeah.

685

:

So as a first time I do that in the

podcast, but I had that.

686

:

Yeah.

687

:

So I'm a big coffee drinker.

688

:

I love coffee.

689

:

I'm a big coffee nerd.

690

:

But from time to time, I try to decrease

my caffeine usage, you know, also because

691

:

you have some habituation effects.

692

:

So if I want to keep the caffeine shot

effect, well, I have to sometimes do a

693

:

decrease of my usage.

694

:

And funnily enough, when I was thinking

about that, a small company called Magic

695

:

Mind, they came to me...

696

:

They sent me an email and they listened to

the show and they were like, hey, you've

697

:

got a cool show.

698

:

I would be happy to send you some bottles

for you to try and to talk about it on the

699

:

show.

700

:

And I thought that was fun.

701

:

So I got some Magic Mind myself.

702

:

I drank it, but I'm not going to buy

Jonathan because I got Magic Mind to send

703

:

some samples to Jonathan.

704

:

And if you are watching the YouTube video,

Jonathan is going to try the Magic Mind

705

:

right now, live.

706

:

So yeah, take it away, Jon.

707

:

Yeah, this is interesting because you

reached out to me for the podcast and I

708

:

had not met you, but you know, it's a

conversation, it's a podcast, you have to

709

:

do great work.

710

:

Yes, I'll say yes to that.

711

:

Then you said, how would you like to try

the Magic Mind?

712

:

And I thought...

713

:

being a psych major as an undergraduate,

this is an interesting social psychology

714

:

experiment where a random person from the

internet says, hey, I'll send you

715

:

something.

716

:

So I thought there's a little bit of

safety in that by drinking it in front of

717

:

you while we're talking on the podcast.

718

:

But of course, I know you can cut this out

if I hit the floor, but here it comes.

719

:

So you're drinking it like, sure.

720

:

Yeah, I decided to drink it like a shot,

if you will.

721

:

It was actually tasted much better than I

expected.

722

:

It came in a bottle with green.

723

:

It tasted tangy, so very good.

724

:

And now the question will be, if I get

better at my answers to your questions by

725

:

the end of the podcast, therefore we have

now a nice experiment.

726

:

But no, I noticed it has a bit of

caffeine, certainly less than a cup of

727

:

coffee.

728

:

But at the same time, it doesn't seem

offensive whatsoever.

729

:

Yeah, that's pretty good.

730

:

Yeah, I mean, I'm still drinking caffeine,

if that's all right.

731

:

But yeah, from time to time, I like to

drink it.

732

:

My habituation, my answer to that is just

drink more.

733

:

That's fine.

734

:

Yeah, exactly.

735

:

Oh yeah, and decaf and stuff like that.

736

:

But yeah, I love the idea of the product

is cool.

737

:

I liked it.

738

:

So I was like, yeah, I'm going to give it

a shot.

739

:

And so the way I drank it was also

basically making myself a latte

740

:

coffee, I would use the Magic Pint and

then I would put my milk in the milk foam.

741

:

And that is really good.

742

:

I have to say.

743

:

See how that works.

744

:

Yeah.

745

:

So it's based on, I mean, the thing you

taste most is the matcha, I think.

746

:

And usually I'm not a big fan of matcha

and that's why I give it the green color.

747

:

I think usually I'm not, but I had to say,

I really appreciated that.

748

:

You and me both, I was feeling the same

way.

749

:

When I saw it come in the mail, I was

like, ooh, that added to my skepticism,

750

:

right?

751

:

I'm trying to be a good scientist.

752

:

I'm trying to be like, yeah.

753

:

But yeah, it was actually surprisingly,

tasted more like a juice, like a citrus

754

:

juice than it was matcha.

755

:

So it was much nicer than I expected.

756

:

Yeah, I love that because me too, I'm

obviously extremely skeptical about all

757

:

those stuff.

758

:

So.

759

:

I like doing that.

760

:

It's way better, way more fun to do it

with you or any other nerd from the

761

:

community than doing it with normal people

from the street because I'm way too

762

:

skeptical for them.

763

:

They wouldn't even understand my

skepticism.

764

:

I agree.

765

:

I felt like in a scientific community,

I've seen some of the people you've had on

766

:

the podcast, we're all a little bit

skeptical about what we do.

767

:

I could bring that skepticism here and I'd

feel like at home, hopefully.

768

:

I'm glad that you allowed me to do that.

769

:

Yeah.

770

:

And that's the way of life.

771

:

Thanks for trusting me because I agree

that seeing from a third party observer,

772

:

you'd be like, that sounds like a scam.

773

:

That guy is just inviting me to sell him

something to me.

774

:

In a week, he's going to send me an email

to tell me he's got some financial

775

:

troubles and I have to wire him $10,000.

776

:

Waiting for that or is it, what level of

paranoia do I have this morning?

777

:

I was like, well, who are my enemies and

who really wants to do something bad to

778

:

me?

779

:

Right?

780

:

So, I don't believe I'm at that level.

781

:

So I don't think I have anything to worry

about.

782

:

It seems like a reputable company.

783

:

So it was, it was amazing.

784

:

Yeah.

785

:

No, that was good.

786

:

Thanks a lot MagicMine for sending me

those samples, that was really fun.

787

:

Feel free to give it a try, other people

if you want, if that sounded like

788

:

something you'd be interested in.

789

:

And if you have any other product to send

me, send them to me, I mean, that sounds

790

:

fun.

791

:

I mean, I'm not gonna say yes to

everything, you know, I have standards on

792

:

the show, and especially scientific

standards.

793

:

But you can always send me something.

794

:

And I will always analyze it.

795

:

You know, somehow you can work out an

agreement with the World Cup, right?

796

:

Some World Cup tickets for the next time.

797

:

True.

798

:

That would be nice.

799

:

True.

800

:

Yeah, exactly.

801

:

Awesome.

802

:

Well, what we did is actually kind of

related, I think, I would say to the

803

:

other, another aspect of your work.

804

:

And that is model comparison.

805

:

So, and it's again, a topic that's asked a

lot by students.

806

:

Especially when they come from the

classical machine learning framework where

807

:

model comparison is just everywhere.

808

:

So often they ask how they can do that in

the Bayesian framework.

809

:

Again, as usual, I am always skeptical

about just doing model comparison and just

810

:

picking your model based on some one

statistic.

811

:

I always say there is no magic one

matching bullet, you know, in the Bayesian

812

:

framework where it's just, okay, model

comparisons say that, so for sure.

813

:

That's the best model.

814

:

I wouldn't say that's how it works.

815

:

And you would need a collection of

different indicators, including, for

816

:

instance, the LOO, the LOO factor, that

tells you, yeah, that model is better.

817

:

But not only that, what about the

posterior predictions?

818

:

What about the model structure?

819

:

What about the priors?

820

:

What about just the generative story about

the model?

821

:

But talking about model comparison, what

can you tell us, John, about the

822

:

some best practices for carrying out

effective model comparisons?

823

:

Kajen is best practice.

824

:

I'll just give you what my practice is.

825

:

I will make no claim that it's best.

826

:

It's difficult.

827

:

I think you hit on all the aspects of it

in introducing the topic.

828

:

If you have a set of models that you're

considering, the first thing I'd like to

829

:

think about is not the comparison between

them as much as how each model would fit a

830

:

data set of data

831

:

post-serial predictive model checking is,

you know, from an amazing sense is where

832

:

really a lot of the work for me is focused

around.

833

:

Interestingly, what you choose to check

against is a bit of a challenge,

834

:

particularly, you know, in certain fields

in psychometrics, at least the ones I'm

835

:

familiar with.

836

:

I do see a lot of, first of all, model

fit,

837

:

well-researched area in psychometrics in

general.

838

:

Really, there's millions of papers in the

:

839

:

like that many.

840

:

And then another, it's always been

something that people have studied.

841

:

I think recently there's been a resurgence

of new ideas in it as well.

842

:

So it's well-covered territory from the

psychometric literature.

843

:

It's less well-covered, at least in my

view, in Bayesian psychometrics.

844

:

So what I've tried to do,

845

:

with my work to try to see if a model fits

absolutely is to look at, there's this,

846

:

one of the complicating factors is that a

lot of my data is discrete.

847

:

So it's correct and correct scored items.

848

:

And in that sense, in the last 15, 20

years, there's been some good work in the

849

:

non-Bayesian world about how to use what

we call limited information methods to

850

:

assess model fit.

851

:

So instead of,

852

:

looking at model fit to the entire

contingency table.

853

:

So if you have a set of binary data, let's

say 10 variables that you've observed,

854

:

technically you have 1,024 different

probabilities that have permutations of

855

:

ways they could be zeros and ones.

856

:

And model fit should be built toward that

1,024 vector of probabilities.

857

:

Good luck with that, right?

858

:

You're not gonna collect enough data to do

that.

859

:

And so...

860

:

What a group of scientists Alberto Medeo

Alavarez, Lissai and others have created

861

:

are sort of model fit to lower level

contingency tables.

862

:

So each marginal moment of the day, each

mean effectively, and then like a two-way

863

:

table between all pairs of observed

variables.

864

:

In work that I've done with a couple of

students recently, we've tried to

865

:

replicate that idea, but more on a

Bayesian sentence.

866

:

So could we come up with

867

:

and M, like a statistic, this is called an

M2 statistic.

868

:

Could we come up with a version of a

posterior predictive check for what a

869

:

model says the two-way table should look

like?

870

:

And then similar to that, could we create

a model such that we know saturates that?

871

:

So for instance, if we have 10 observed

variables, we could create a model that

872

:

has all 10 shoes to two-way tables

estimated perfect, what we would expect to

873

:

be perfect.

874

:

Now, of course, there's posterior

distributions, but you would expect with

875

:

you know, plenty of data and, you know,

very diffused priors that you would get

876

:

point estimates, EAP estimates, and that

should be right about where you can

877

:

observe the frequencies of data.

878

:

Quick check.

879

:

So, um, the idea then is now we have two

models, one of which we know should fit

880

:

the data absolutely.

881

:

And one of which we know, uh, we're, we're

wondering if it fits now that the

882

:

comparison comes together.

883

:

So we have these two predictive

distributions.

884

:

Um, how do we compare them?

885

:

Uh, and that's where, you know,

886

:

different approaches we've taken.

887

:

One of those is just simply looking at the

distributional overlaps.

888

:

We tried to calculate a, we use the

Kilnogorov Smirnov distribution, sort of

889

:

the sea where moments are percent wise of

the distributions with overlap, because if

890

:

your model's data overlaps with what you

think that the data should look like, you

891

:

think the model fits well.

892

:

And if it doesn't, it should be far apart

and won't fit well.

893

:

That's how we've been trying to build.

894

:

It's weird because it's a model

comparison, but one of the comparing

895

:

models we know to be

896

:

what we call saturated, it should fit the

data the best and no other model, all the

897

:

other models should be subsumed into it.

898

:

So that's the approach I've taken recently

with posterior predictive checks, but then

899

:

a model comparison.

900

:

We could have used, as you mentioned, the

LOO factor or the LOO statistic.

901

:

And maybe that's something that we should

look into also.

902

:

We haven't yet, but one of my recent

graduates, new assistant professor at

903

:

University of Arkansas here in the United

States.

904

:

Ji Hang Zhang had done a lot of work on

this in his dissertation and other studies

905

:

here.

906

:

So that's sort of the approach I take.

907

:

The other thing I want to mention though

is when you're comparing amongst models,

908

:

you have to establish that model for that

absolute fit first.

909

:

So the way I envision this is you sort of

compare your model to this sort of

910

:

saturated model.

911

:

You do that for multiple versions of your

models and then effectively choose amongst

912

:

the set of models you're comparing that

sort of fit.

913

:

But what that absolute fit is, is like you

mentioned, it's nearly impossible to tell

914

:

exactly.

915

:

There's a number of ideas that go into

what makes a good for a good fitting

916

:

model.

917

:

Yeah.

918

:

And definitely I encourage people to go

take a look at the Lou paper.

919

:

I will put a link in the show note to that

paper.

920

:

And also if you're using Arvies, whether

in Julia or Python, we do have.

921

:

implementation of the Loo algorithm.

922

:

So comparing your models with obviously

extremely simple, it's just a call to

923

:

compare and then you can even do a plot of

that.

924

:

And yeah, as you were saying, the Loo

algorithm doesn't have any meaning by

925

:

itself.

926

:

Right?

927

:

The Loo score of a model doesn't mean

anything.

928

:

It's in comparison to another, to other

models.

929

:

So yeah, basically having a baseline model

that you think is already good enough.

930

:

And then all the other models have to

compare to that one, which basically could

931

:

be like the placebo, if you want, or the

already existing solution that there is

932

:

for that.

933

:

And then any model that's more complicated

than that should be in competition with

934

:

that one and should have a reason to be

used, because otherwise, why are you using

935

:

a more complicated model if you could just

use

936

:

a simple linear regression, because that's

what I use most of the time for my

937

:

baseline model.

938

:

Right?

939

:

Baseline model, just use a simple linear

regression, and then do all the fancy

940

:

modeling you want and compare that to the

linear regression, both in predictions and

941

:

with the Loo algorithm.

942

:

And well, if there is a good reason to

make your life more difficult, then use

943

:

it.

944

:

But otherwise, why would you?

945

:

And yeah, actually talking about these

complexities, something I see is also that

946

:

many, many people, many practitioners

might be hesitant to adopt the patient

947

:

methods due to the fact that they perceive

them as complex.

948

:

So I'm wondering yourself, what resources

or strategies would you recommend to those

949

:

who want to learn and apply patient

techniques in their research?

950

:

And especially in your field of

psychometrics.

951

:

Yeah.

952

:

I think, um, starting with an

understanding of sort of just the output,

953

:

you know, the basics of if you're, if you

have data and if your responsibility is

954

:

providing analysis for it, uh, finding

either a package or somebody else's

955

:

program that makes the coding quick.

956

:

So like you've mentioned linear

regression, if you use VRMS and R, you

957

:

know, which will translate that into Stan.

958

:

You can quickly go about getting a

Bayesian result fast.

959

:

And I found that to me, the conceptual

consideration of what a posterior

960

:

distribution is actually is less complex

than we think about when we think about

961

:

all the things that we're drilled into in

the classical methods, like, you know,

962

:

what, where does the standard error come

from and all this other, you know,

963

:

asymptotic features in Bayes it's, it's

visible, like you can see a posterior

964

:

distribution, you can plot it, you can,

you know, touch it, almost like touch it

965

:

and feel it, right?

966

:

It's right there in front of you.

967

:

So for me, I think the thing I try to get

people to first is just to understand what

968

:

the outputs are.

969

:

Sort of what are the key parts of it.

970

:

And then, you know, hopefully that gives

that mental representation of where that,

971

:

where they're moving toward.

972

:

And then at that point, start to add in

all the complexities.

973

:

Um, but it is, I think it's, it's

incredibly challenging to try to, to teach

974

:

Bayesian methods and I actually think the

further along a person goes, not learning

975

:

the Bayesian version of things.

976

:

Makes it even harder because now you have

all this well-established, um, can we say

977

:

routines or statistics that you're used to

seeing that are not Bayesian, uh, that may

978

:

or may not have a direct, um, analog in

the Bayes world.

979

:

Um, but that may not be a bad thing.

980

:

So, um, thinking about it, actually, I'm

going to take a step back here.

981

:

Can conceptually, I think it's, this is

the challenge, um, we face in a program

982

:

like I do right here.

983

:

I'm working right now.

984

:

I work with, um, nine other tenure track.

985

:

or Tender to Tender Tech faculty, which is

a very large program.

986

:

And we have a long-running curriculum, but

sort of the question I like to ask is,

987

:

what do we do with Bayes?

988

:

Do we have a parallel track in Bayes?

989

:

Do we do Bayes in every class?

990

:

Because that's a heavy lift for a lot of

people as well.

991

:

Right now, it's, I teach the Bayes

classes, and occasionally some of my

992

:

colleagues will put Bayesian statistics in

their classes, but it's tough.

993

:

I think if I were

994

:

you know, anointed myself king of how we

do all the curriculum.

995

:

I don't know the answer I'd come to.

996

:

I go back and forth each way.

997

:

So, um, I would love to see what a

curriculum looks like where they only

998

:

started with base and only kept it in

base.

999

:

Cause I think that would be a lot of fun.

:

00:57:32,723 --> 00:57:35,665

Um, and the quit, the thought question I

asked myself that I don't have an answer

:

00:57:35,665 --> 00:57:40,488

for is would that be a better mechanism to

get students up to speed on the models

:

00:57:40,488 --> 00:57:45,251

they're using, then it would be in other

contexts and other classical contexts, I

:

00:57:45,251 --> 00:57:45,832

don't, I don't know.

:

00:57:45,832 --> 00:57:47,873

Yeah.

:

00:57:47,873 --> 00:57:48,398

Yay.

:

00:57:48,398 --> 00:57:49,258

Good point.

:

00:57:49,859 --> 00:57:51,199

Yeah, two things.

:

00:57:51,199 --> 00:57:54,742

First, King of Curriculum, amazing title.

:

00:57:54,822 --> 00:57:59,145

I think it should actually be renamed to

that title in all campuses around the

:

00:57:59,145 --> 00:57:59,945

world.

:

00:58:00,466 --> 00:58:03,728

The world's worst kingdom is the

curriculum.

:

00:58:03,728 --> 00:58:06,170

Yeah.

:

00:58:06,170 --> 00:58:07,731

I mean, that's really good.

:

00:58:07,731 --> 00:58:10,593

Like you're going to party, you know, and

so what are we doing on King of

:

00:58:10,593 --> 00:58:11,613

Curriculum?

:

00:58:12,494 --> 00:58:15,136

So long as the crown is on the head,

that's all that matters, right?

:

00:58:15,136 --> 00:58:17,477

That would drop some jaws for sure.

:

00:58:23,191 --> 00:58:29,173

And second, I definitely would like the

theory of the multiverse to be true,

:

00:58:29,193 --> 00:58:33,735

because that means in one of these

universes, there is at least one where

:

00:58:33,735 --> 00:58:36,135

Bayesian methods came first.

:

00:58:36,315 --> 00:58:42,197

And I am definitely curious to see what

that world looks like and see how...

:

00:58:42,657 --> 00:58:43,550

Yeah, what...

:

00:58:43,550 --> 00:58:47,912

What's that world where people were

actually exposed to patient methods first

:

00:58:47,933 --> 00:58:50,955

and maybe to frequency statistics later?

:

00:58:50,955 --> 00:58:56,398

Were they actually exposed to frequency

statistics later?

:

00:58:56,398 --> 00:58:57,619

That's the question.

:

00:58:57,739 --> 00:59:01,341

No, but yeah, jokes aside, I would be

definitely curious about that.

:

00:59:02,302 --> 00:59:07,266

Yeah, well, I don't know that I'll have

that experiment in my lifetime, but maybe

:

00:59:07,266 --> 00:59:09,727

like in a parallel universe somewhere.

:

00:59:15,010 --> 00:59:22,713

Before we close up the show, I'm wondering

if you have a personal anecdote or example

:

00:59:22,713 --> 00:59:27,315

of a challenging problem you encountered

in your research or teaching related to

:

00:59:27,315 --> 00:59:30,817

vision stats and how you were able to

navigate through it?

:

00:59:30,817 --> 00:59:30,917

Yeah.

:

00:59:30,917 --> 00:59:40,301

I mean, maybe it's too much in the weeds,

but that first experience I was in

:

00:59:40,301 --> 00:59:41,941

graduate school trying to learn.

:

00:59:45,151 --> 00:59:45,631

code.

:

00:59:45,631 --> 00:59:53,176

It was coding a correlation matrix of

tetrachore correlations.

:

00:59:53,176 --> 00:59:56,657

And that was incredibly difficult.

:

00:59:57,138 --> 01:00:02,021

One day, one of my colleagues, Bob Henson,

figured it out with the likelihood

:

01:00:02,021 --> 01:00:02,841

function and so forth.

:

01:00:02,841 --> 01:00:04,882

But that was the holdup that we had.

:

01:00:05,723 --> 01:00:09,910

And it's incredible because I say this

because again, we're not, I mentioned it.

:

01:00:09,910 --> 01:00:11,630

do a lot of my own package coding or

whatnot.

:

01:00:11,630 --> 01:00:16,473

But I think you see a similar phenomenon

if you misspecify something in your model

:

01:00:16,473 --> 01:00:20,996

in general and you get results and the

results are either all over the place or

:

01:00:20,996 --> 01:00:21,776

entire number line.

:

01:00:21,776 --> 01:00:24,858

For me, it was the correlations, posterior

distribution looked like a uniform

:

01:00:24,858 --> 01:00:26,339

distribution from negative one to one.

:

01:00:26,339 --> 01:00:28,980

That was, that's a bad thing to see,

right?

:

01:00:28,980 --> 01:00:35,884

So just the, the anecdote I have with this

is, it's less, I guess it's less like

:

01:00:35,884 --> 01:00:38,318

awesome, like when you're like, oh, Bayes

did this and then.

:

01:00:38,318 --> 01:00:42,339

couldn't have done it otherwise, but it's

more the perseverance that goes to

:

01:00:42,339 --> 01:00:47,981

sticking with the Bayesian side, which is,

um, Bayes also provides you the ability to

:

01:00:47,981 --> 01:00:53,083

check a little bit of your work to see if

it's completely gone sideways.

:

01:00:53,083 --> 01:00:53,404

Right.

:

01:00:53,404 --> 01:00:55,404

So, uh, you see a result like that.

:

01:00:55,404 --> 01:00:57,665

You have that healthy dose of skepticism.

:

01:00:57,865 --> 01:01:02,727

You start to investigate more in my case,

it took years, a couple of years of my

:

01:01:02,727 --> 01:01:08,169

life, uh, working in concert with other

people, uh, as grad students, but, um,

:

01:01:08,242 --> 01:01:10,544

was fixed, it was almost obvious that it

was.

:

01:01:10,544 --> 01:01:15,488

I mean, it was, you went from this uniform

distribution across negative one to one to

:

01:01:15,488 --> 01:01:18,010

something that looked very much like a

posterior distribution that we're used to

:

01:01:18,010 --> 01:01:21,192

seeing, send around a certain value of the

correlation.

:

01:01:21,373 --> 01:01:25,957

And again, it was, for us, it was figuring

out what the likelihood was, but for most

:

01:01:25,957 --> 01:01:27,738

packages, at least that's not a big deal.

:

01:01:27,738 --> 01:01:31,161

I think it's already specified in your

choice of model and prior.

:

01:01:31,201 --> 01:01:36,185

But at the same time, just remembering

that

:

01:01:36,270 --> 01:01:40,031

Uh, it's sort of the, the frustration part

of it, not making it work is actually

:

01:01:40,031 --> 01:01:40,791

really informative.

:

01:01:40,791 --> 01:01:44,472

Uh, you get that and you, you can build

and you can sort of check your work if you

:

01:01:44,472 --> 01:01:45,912

go forward analytically.

:

01:01:45,912 --> 01:01:50,153

I mean, not analytically brute force, the

sampling part, but that's sort of a check

:

01:01:50,153 --> 01:01:51,174

on your work.

:

01:01:51,794 --> 01:01:57,235

Trying to say, so not a great example, not

a super inspiring example, but, um, more

:

01:01:57,235 --> 01:01:59,536

perseverance pays off in days and in life.

:

01:01:59,536 --> 01:02:01,617

So it's sort of the analog that I get from

it.

:

01:02:01,617 --> 01:02:03,037

Yeah.

:

01:02:03,037 --> 01:02:04,377

Yeah, no, for sure.

:

01:02:04,377 --> 01:02:05,297

I mean, um,

:

01:02:06,066 --> 01:02:11,950

is perseverance is so important because

you're definitely going to encounter

:

01:02:12,091 --> 01:02:12,411

issues.

:

01:02:12,411 --> 01:02:18,336

I mean, none of your models is going to

work as you thought it would.

:

01:02:18,336 --> 01:02:23,400

So if you don't have that drive and that

passion for the thing that you're

:

01:02:23,400 --> 01:02:30,466

standing, it's going to be extremely hard

to just get it through the finish line

:

01:02:30,466 --> 01:02:32,267

because it's not going to be easy.

:

01:02:32,267 --> 01:02:35,186

So, you know, it's like choosing a new

sport.

:

01:02:35,186 --> 01:02:40,867

If you don't like what the sport is all

about, you're not going to stick with it

:

01:02:40,867 --> 01:02:42,788

because it's going to be hard.

:

01:02:42,788 --> 01:02:51,370

So that perseverance, I would say, come

from your curiosity and your passion for

:

01:02:51,510 --> 01:02:54,351

your field and the methods you're using.

:

01:02:54,851 --> 01:02:57,592

And the other thing I was going to add,

this is tangential, but let me just add

:

01:02:57,592 --> 01:03:01,553

it, you have the chance to go visit Bay's

grave in London, take it.

:

01:03:01,553 --> 01:03:03,570

I had to do that last summer.

:

01:03:03,570 --> 01:03:06,891

I just, I was in London, I had my children

with me and we all picked some spot we

:

01:03:06,891 --> 01:03:07,851

wanted to go to.

:

01:03:07,851 --> 01:03:12,373

And I was like, I'm going to go find and

take a picture in front of Bayes grave.

:

01:03:12,373 --> 01:03:14,234

And I sort of brought up an interesting

question.

:

01:03:14,234 --> 01:03:18,136

Like I don't know the etiquette of taking

photographs in front of a deceased grave

:

01:03:18,136 --> 01:03:18,756

site.

:

01:03:18,756 --> 01:03:20,736

This is at least providing it.

:

01:03:21,417 --> 01:03:25,298

But then ironically, as you're sitting

there, as I was sitting there on the tube,

:

01:03:25,499 --> 01:03:29,700

leaving, I sat next to a woman and she had

Bayes theorem on her shirt.

:

01:03:29,700 --> 01:03:31,681

It was the Bayes School of Economics.

:

01:03:31,681 --> 01:03:32,874

So something like this.

:

01:03:32,874 --> 01:03:36,757

in London, I was like, it was like, okay,

I have reached the Mecca.

:

01:03:36,757 --> 01:03:41,722

Like the perseverance led to like, like a

trip, you know, my own version of the trip

:

01:03:41,722 --> 01:03:42,983

to, to London.

:

01:03:42,983 --> 01:03:45,465

Uh, but definitely, uh, definitely worth

the time to go.

:

01:03:45,465 --> 01:03:49,669

If you want to be surrounded, uh, once you

reach that, that level of perseverance,

:

01:03:49,669 --> 01:03:52,271

uh, you're part of the club and then you

can do things like that.

:

01:03:52,711 --> 01:03:56,475

Fine vacations around, you know, holidays

around base, base graves.

:

01:03:56,475 --> 01:03:59,117

Yeah.

:

01:03:59,117 --> 01:03:59,877

I mean.

:

01:03:59,970 --> 01:04:02,811

I am definitely gonna do that.

:

01:04:02,811 --> 01:04:06,732

Thank you very much for giving me another

idea of a nerd holiday.

:

01:04:06,732 --> 01:04:10,894

My girlfriend is gonna hate me, but she

always wanted to visit London, so you

:

01:04:10,894 --> 01:04:12,755

know, that's gonna be my bait.

:

01:04:13,796 --> 01:04:17,417

It's not bad to get to, it's off of Old

Street, you know, actually well marked.

:

01:04:17,417 --> 01:04:21,059

I mean the grave site's a little

weathered, but it's in a good spot, a good

:

01:04:21,059 --> 01:04:25,341

part of town, so you know, not really

heavily touristy, amazingly.

:

01:04:25,341 --> 01:04:26,401

Oh yeah, I'm guessing.

:

01:04:26,401 --> 01:04:27,381

But you know.

:

01:04:28,314 --> 01:04:30,355

I am guessing that's the good thing.

:

01:04:31,015 --> 01:04:34,697

Yeah, no, I already know how I'm gonna ask

her.

:

01:04:34,697 --> 01:04:36,238

Honey, when I go to London?

:

01:04:36,278 --> 01:04:36,898

Perfect.

:

01:04:36,898 --> 01:04:37,599

Let's go to Bay's.

:

01:04:37,599 --> 01:04:38,579

Let's go check out Bay's Grave.

:

01:04:38,579 --> 01:04:42,362

Yeah, I mean, that's perfect.

:

01:04:42,362 --> 01:04:43,882

That's amazing.

:

01:04:43,882 --> 01:04:48,045

So say, I mean, you should send me that

picture and that should be your picture

:

01:04:48,045 --> 01:04:49,746

for these episodes.

:

01:04:49,746 --> 01:04:55,409

I always take a picture from guests to

illustrate the episode icon, but you

:

01:04:55,409 --> 01:04:57,130

definitely need that.

:

01:04:57,130 --> 01:04:58,190

picture for your icon.

:

01:04:58,190 --> 01:04:58,590

I can do that.

:

01:04:58,590 --> 01:05:00,211

I'll be happy to.

:

01:05:00,211 --> 01:05:01,212

Yeah.

:

01:05:01,492 --> 01:05:02,652

Awesome.

:

01:05:03,113 --> 01:05:03,573

Definitely.

:

01:05:03,573 --> 01:05:08,856

So before asking you the last two

questions, I'm just curious how you see

:

01:05:09,036 --> 01:05:15,620

the future of patient stats in the context

of psychological sciences and

:

01:05:15,620 --> 01:05:16,740

psychometrics.

:

01:05:16,981 --> 01:05:22,684

And what are some exciting avenues for

research and application that you envision

:

01:05:22,684 --> 01:05:25,705

in the coming years or that you would

really like to see?

:

01:05:26,494 --> 01:05:28,754

Oh, that's a great question.

:

01:05:28,754 --> 01:05:29,134

Terrible.

:

01:05:29,134 --> 01:05:37,357

So I, you know, interestingly, in

psychology, you know, quantitative

:

01:05:37,357 --> 01:05:41,338

psychology sort of been on a downhill

swing for, I don't know,:

:

01:05:41,338 --> 01:05:44,278

there's fewer and fewer programs, at least

in the United States, where people are

:

01:05:44,278 --> 01:05:45,139

training.

:

01:05:45,219 --> 01:05:49,020

But despite that, I feel like the use of

Bayesian statistics is up in a lot of a

:

01:05:49,020 --> 01:05:50,260

lot of different other areas.

:

01:05:50,260 --> 01:05:55,382

And I think that I think that affords a

bit.

:

01:05:55,382 --> 01:05:56,922

better model-based science.

:

01:05:56,922 --> 01:06:00,384

So you have to specify a model, you have

to model in mind, and then you go and do

:

01:06:00,384 --> 01:06:00,584

that.

:

01:06:00,584 --> 01:06:03,705

I think that benefit makes the science

much better.

:

01:06:03,705 --> 01:06:07,607

You're not just using sort of what's

always been done.

:

01:06:07,607 --> 01:06:10,848

You can sort of push the envelope

methodologically a bit more.

:

01:06:10,848 --> 01:06:14,109

And I think that that, and Bayesian

statistics in one way, another benefit of

:

01:06:14,109 --> 01:06:18,291

them is now you can code an algorithm that

likely will work without having to know,

:

01:06:18,291 --> 01:06:21,952

like you said, all of the underpinnings,

the technical side of things, you can use

:

01:06:21,952 --> 01:06:24,453

an existing package to do so.

:

01:06:25,670 --> 01:06:29,751

I like to say that that's going to

continue to make science a better

:

01:06:29,751 --> 01:06:30,812

practice.

:

01:06:31,332 --> 01:06:39,635

I think the fear that I have is sort of

the sea of the large language model-based

:

01:06:39,676 --> 01:06:43,137

version of what we're doing in machine

learning, artificial intelligence.

:

01:06:43,137 --> 01:06:49,360

But I will be interested to see how we

incorporate a lot of the Bayesian ideas,

:

01:06:49,360 --> 01:06:51,801

Bayesian methods into that as well.

:

01:06:51,801 --> 01:06:53,581

I think that there's potential.

:

01:06:53,846 --> 01:06:57,527

Clearly, people are doing this, I mean,

that's what runs a lot of what is

:

01:06:57,527 --> 01:06:58,608

happening anyway.

:

01:06:58,608 --> 01:07:00,948

So I look forward to seeing that as well.

:

01:07:01,349 --> 01:07:07,351

So I get a sense that what we're talking

about is really what may be the foundation

:

01:07:07,351 --> 01:07:08,872

for what the future will be.

:

01:07:08,872 --> 01:07:12,033

I mean, maybe we will, maybe instead of

that parallel universe, if we could come

:

01:07:12,033 --> 01:07:16,615

back or go into the future just in our own

universe in 50 years, maybe what we will

:

01:07:16,615 --> 01:07:19,356

see is curriculum entirely on Bayesian

methods.

:

01:07:19,356 --> 01:07:21,966

And from, you know, I just looked at your.

:

01:07:21,966 --> 01:07:26,027

topic list you had recently talking about

variational inference and so forth.

:

01:07:26,387 --> 01:07:32,910

The use of that in very large models

themselves, I think that is very important

:

01:07:32,910 --> 01:07:33,250

stuff.

:

01:07:33,250 --> 01:07:37,792

So it may just be the thing that crowds

out everything else, but that's

:

01:07:37,792 --> 01:07:42,114

speculative and I don't make a living

making prediction, unfortunately.

:

01:07:42,114 --> 01:07:43,874

So that's the best I can do.

:

01:07:43,874 --> 01:07:45,155

Yeah.

:

01:07:45,155 --> 01:07:46,015

Yeah, yeah.

:

01:07:46,015 --> 01:07:48,756

I mean, that's also more of a wishlist

question.

:

01:07:48,756 --> 01:07:50,297

So that's all good.

:

01:07:50,757 --> 01:07:51,217

Yeah.

:

01:07:51,217 --> 01:07:51,826

Awesome.

:

01:07:51,826 --> 01:07:53,847

Well, John, amazing.

:

01:07:54,708 --> 01:07:55,888

I learned a lot.

:

01:07:55,908 --> 01:07:57,309

We covered a lot of topics.

:

01:07:57,309 --> 01:07:58,670

I'm really happy.

:

01:07:59,531 --> 01:08:04,254

But of course, before letting you go, I'm

going to ask you the last two questions I

:

01:08:04,254 --> 01:08:06,295

ask every guest at the end of the show.

:

01:08:06,836 --> 01:08:10,778

Number one, you had unlimited time and

resources.

:

01:08:10,778 --> 01:08:14,001

Which problem would you try to solve?

:

01:08:14,001 --> 01:08:18,343

Well, I would be trying to figure out how

we know what a student knows every day of

:

01:08:18,343 --> 01:08:21,685

the year so that we can best teach them

where to go next.

:

01:08:22,062 --> 01:08:23,982

That would be it.

:

01:08:23,982 --> 01:08:29,285

Right now, there's not only the problem of

the technical issues of estimation,

:

01:08:29,285 --> 01:08:33,186

there's also the problem of how do we best

assess them, how much time do they spend

:

01:08:33,186 --> 01:08:34,387

doing it and so forth.

:

01:08:34,387 --> 01:08:39,429

That to me is what I would spend most of

my time on.

:

01:08:39,429 --> 01:08:41,510

That sounds like a good project.

:

01:08:41,510 --> 01:08:42,390

I love it.

:

01:08:43,510 --> 01:08:49,633

And second question, if you could have

dinner with any great scientific mind that

:

01:08:49,633 --> 01:08:51,173

life are fictional.

:

01:08:51,234 --> 01:08:52,914

who did be.

:

01:08:52,914 --> 01:08:53,294

All right.

:

01:08:53,294 --> 01:08:55,595

I got a really obscure choice, right?

:

01:08:55,595 --> 01:08:59,016

It's not like I'm picking Einstein or

anything.

:

01:08:59,016 --> 01:09:01,656

I really, I have like two actually, I've

sort of debated.

:

01:09:01,656 --> 01:09:06,238

One is economist Paul Krugman, who writes

for the New York Times, works at City

:

01:09:06,238 --> 01:09:07,418

University of New York now.

:

01:09:07,418 --> 01:09:09,299

You know, Nobel laureate.

:

01:09:09,299 --> 01:09:13,720

Loved his work, loved his understanding

of, for the interplay between model and

:

01:09:13,720 --> 01:09:18,121

data and understanding is fantastic.

:

01:09:18,341 --> 01:09:20,282

So I would just.

:

01:09:20,282 --> 01:09:23,204

sit there and just have to listen to

everything you had to say, I think.

:

01:09:23,224 --> 01:09:26,767

The other is there's a, again, obscure

thing.

:

01:09:26,767 --> 01:09:31,151

One of my things I'm fascinated by is

weather and weather forecasting.

:

01:09:31,151 --> 01:09:35,033

Uh, if you know, I'm in education or

psychological measurement.

:

01:09:35,234 --> 01:09:38,457

Uh, and there's a guy who started the

company called the weather underground.

:

01:09:38,457 --> 01:09:39,738

His name is Jeff Masters.

:

01:09:39,738 --> 01:09:43,941

Uh, you can read his work on a blog at

Yale these days, climate connections,

:

01:09:43,941 --> 01:09:45,262

something along those lines.

:

01:09:45,262 --> 01:09:49,385

Anyway, since sold the company, but he's

fascinating about modeling, you know,

:

01:09:49,546 --> 01:09:52,148

Right now we're in the peak of hurricane

season in the United States.

:

01:09:52,148 --> 01:09:56,532

We see these storms coming off of Africa

or spinning up everywhere and sort of the

:

01:09:56,532 --> 01:10:01,416

interplay between, unfortunately, the

climate change and then other atmospheric

:

01:10:01,416 --> 01:10:01,996

dynamics.

:

01:10:01,996 --> 01:10:07,060

This just makes for an incredibly complex

system that's just fascinating and how

:

01:10:07,201 --> 01:10:08,742

science approaches prediction there.

:

01:10:08,742 --> 01:10:10,404

So I find that to be great.

:

01:10:10,404 --> 01:10:11,464

But those are the two.

:

01:10:11,464 --> 01:10:14,107

I had to think a lot about that because

there's so many choices, but those two

:

01:10:14,107 --> 01:10:17,769

people are the ones I read the most,

certainly when it's not just in my field.

:

01:10:18,942 --> 01:10:19,702

Nice.

:

01:10:19,702 --> 01:10:21,983

Yeah, sounds fascinating.

:

01:10:22,063 --> 01:10:24,505

And weather forecasting is definitely

incredible.

:

01:10:25,445 --> 01:10:30,188

Also, because the great thing is you have

feedback every day.

:

01:10:30,828 --> 01:10:33,010

So that's really cool.

:

01:10:33,010 --> 01:10:34,070

You can improve your predictions.

:

01:10:34,070 --> 01:10:35,751

Like the missing data problem.

:

01:10:35,992 --> 01:10:37,973

You can't sample every part of the

atmosphere.

:

01:10:37,973 --> 01:10:41,895

So how do you incorporate that into your

analysis as well?

:

01:10:42,615 --> 01:10:43,856

No, that's incredible.

:

01:10:43,856 --> 01:10:45,697

Multiple average models and stuff.

:

01:10:45,697 --> 01:10:46,646

Anyway, yeah.

:

01:10:46,646 --> 01:10:51,529

Yeah, that's also a testimony to the power

of modeling and parsimony, you know, where

:

01:10:51,529 --> 01:10:56,533

it's like, because I worked a lot on

electoral forecasting models and, you

:

01:10:56,533 --> 01:11:01,937

know, classic way people dismiss models in

these areas.

:

01:11:01,937 --> 01:11:06,340

Well, you cannot really predict what

people are going to do at an individual

:

01:11:06,340 --> 01:11:08,061

level, which is true.

:

01:11:08,061 --> 01:11:11,624

I mean, you cannot, people have free will,

you know, so you cannot predict at an

:

01:11:11,624 --> 01:11:14,705

individual level what they are going to

do, but you can.

:

01:11:14,766 --> 01:11:19,249

quite reliably predict what masses are

going to do.

:

01:11:19,329 --> 01:11:27,836

Yeah, basically, where the aggregation of

individual points, you can actually kind

:

01:11:27,836 --> 01:11:30,077

of reliably do it.

:

01:11:30,939 --> 01:11:35,002

And so the power of modeling here where

you get something that, yeah, you know,

:

01:11:35,002 --> 01:11:36,143

it's not good.

:

01:11:36,143 --> 01:11:44,829

It's, you know, the model is wrong, but it

works because it simplifies

:

01:11:45,378 --> 01:11:51,541

things, but doesn't simplify them to a

point where it doesn't make sense anymore.

:

01:11:51,801 --> 01:11:55,783

Kind of like the standard model in

physics, where we know it doesn't work, it

:

01:11:55,783 --> 01:12:02,027

breaks at some point, but it does a pretty

good job of predicting a lot of phenomena

:

01:12:02,027 --> 01:12:02,527

and we observe.

:

01:12:02,527 --> 01:12:04,988

So, do you prefer that?

:

01:12:04,988 --> 01:12:09,431

Is it free will or is it random error?

:

01:12:09,431 --> 01:12:11,852

Well, you have to come back for another

episode on that because otherwise, yes.

:

01:12:11,852 --> 01:12:13,893

That's a good one.

:

01:12:16,547 --> 01:12:16,787

Good point.

:

01:12:16,787 --> 01:12:16,888

Nice.

:

01:12:16,888 --> 01:12:22,172

Well, Jonathan, thank you so much for your

time.

:

01:12:22,172 --> 01:12:26,835

As usual, I will put resources and a link

to your website in the show notes for

:

01:12:26,835 --> 01:12:28,336

those who want to dig deeper.

:

01:12:28,436 --> 01:12:31,819

Thank you again, Jonathan, for taking the

time and being on this show.

:

01:12:32,440 --> 01:12:32,940

Happy to be here.

:

01:12:32,940 --> 01:12:34,521

Thanks for the opportunity.

:

01:12:34,521 --> 01:12:41,947

It was a pleasure to speak with you and I

hope it makes sense for a lot of people.

:

01:12:41,947 --> 01:12:43,488

Appreciate your time.

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