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Takeaways:

• Teaching Bayesian Concepts Using M&Ms: Tomi Capretto uses an engaging classroom exercise involving M&Ms to teach Bayesian statistics, making abstract concepts tangible and intuitive for students.
• Practical Applications of Bayesian Methods: Discussion on the real-world application of Bayesian methods in projects at PyMC Labs and in university settings, emphasizing the practical impact and accessibility of Bayesian statistics.
• Contributions to Open-Source Software: Tomi’s involvement in developing Bambi and other open-source tools demonstrates the importance of community contributions to advancing statistical software.
• Challenges in Statistical Education: Tomi talks about the challenges and rewards of teaching complex statistical concepts to students who are accustomed to frequentist approaches, highlighting the shift to thinking probabilistically in Bayesian frameworks.
• Future of Bayesian Tools: The discussion also touches on the future enhancements for Bambi and PyMC, aiming to make these tools more robust and user-friendly for a wider audience, including those who are not professional statisticians. 

Chapters:

05:36 Tomi's Work and Teaching

10:28 Teaching Complex Statistical Concepts with Practical Exercises

23:17 Making Bayesian Modeling Accessible in Python

38:46 Advanced Regression with Bambi

41:14 The Power of Linear Regression

42:45 Exploring Advanced Regression Techniques

44:11 Regression Models and Dot Products

45:37 Advanced Concepts in Regression

46:36 Diagnosing and Handling Overdispersion

47:35 Parameter Identifiability and Overparameterization

50:29 Visualizations and Course Highlights

51:30 Exploring Niche and Advanced Concepts

56:56 The Power of Zero-Sum Normal

59:59 The Value of Exercises and Community

01:01:56 Optimizing Computation with Sparse Matrices

01:13:37 Avoiding MCMC and Exploring Alternatives

01:18:27 Making Connections Between Different Models

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, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan and Francesco Madrisotti.

Links from the show:

• Tomi’s website: https://tomicapretto.com/
• Tomi on GitHub: https://github.com/tomicapretto
• Tomi on Linkedin: https://www.linkedin.com/in/tom%C3%A1s-capretto-a89873106/
• Tomi on Twitter: https://x.com/caprettotomas
• Advanced Regression course (get 10% off if you’re a Patron of the show): https://www.intuitivebayes.com/advanced-regression
• Bambi: https://bambinos.github.io/bambi/
• LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
• LBS #1 Bayes, open-source and bioinformatics, with Osvaldo Martin: https://learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin/
• patsy - Describing statistical models in Python: https://patsy.readthedocs.io/en/latest/
• formulae - Formulas for mixed-models in Python: https://bambinos.github.io/formulae/
• Introducing Bayesian Analysis With m&m's®: An Active-Learning Exercise for Undergraduates: https://www.tandfonline.com/doi/full/10.1080/10691898.2019.1604106
• Richly Parameterized Linear Models Additive, Time Series, and Spatial Models Using Random Effects https://www.routledge.com/Richly-Parameterized-Linear-Models-Additive-Time-Series-and-Spatial-Models-Using-Random-Effects/Hodges/p/book/9780367533731
• Dan Simpson’s Blog (link to blogs with the ‘sparse matrices’ tag): https://dansblog.netlify.app/#category=Sparse%20matrices
• Repository for Sparse Matrix-Vector dot product: https://github.com/tomicapretto/dot_tests

Transcript

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

Today I am thrilled to host my friend Tommy Capretto, a multifaceted data scientist from

PMC Labs, a dedicated statistics educator at Universidad Nacional de Rosario, and an avid

2

contributor to the open source software community, especially known for his work on Bambi.

3

In our conversation, Tommy shares insights from his dual role as an industry practitioner

and an academic,

4

We dive deep into the practicalities and pedagogical approaches of teaching complex

statistical concepts, making them accessible and engaging.

5

We also explored Tommy's contributions to BEMBEE, which he describes as BRMS for Python.

6

And indeed, it is a Python library designed to make patient modeling more approachable for

beginners and non -experts.

7

This discussion leads us into the heart of our newly launched course,

8

Advanced Regression with Bambi and Pimc, where Tommy, Ravin Kumar and myself unpack the

essentials of regression models, tackle the challenges of parameter identifiability and

9

overparameterization, and address overdispersion and the new zero -sum normal

distribution.

10

So whether you're a student, a professional, or just a curious mind, I'm sure this episode

is packed with insights that will enrich your understanding.

11

statistical world.

12

This is Learn Invasion Statistics, episode 112, recorded June 24, 2024.

13

Welcome Bayesian Statistics, a podcast about Bayesian inference, the methods, the

projects, and the people who make it possible.

14

I'm your host, Alex Andorra.

15

You can follow me on Twitter at alex -underscore

16

like the country.

17

For any info about the show, learnbasedats .com is Laplace to be.

18

Show notes, becoming a corporate sponsor, unlocking Beijing Merch, supporting the show on

Patreon, everything is in there.

19

That's learnbasedats .com.

20

If you're interested in one -on -one mentorship, online courses, or statistical

consulting, feel free to reach out and book a call at topmate .io slash alex underscore

21

and dora.

22

See you around, folks, and best Beijing wishes to you

23

Hello, my dear Vagans!

24

A quick note before today's episode.

25

STANCON 2024 is approaching!

26

It's in Oxford, UK this year from September 9 to 13, and it's shaping up to be an

incredible event for anybody interested in statistical modeling and Vagans in France.

27

Actually, we're currently looking for sponsors to help us offer more scholarships and make

STANCON more accessible to everyone.

28

And we

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encourage you to buy your tickets as soon as possible.

30

Not only will this help with making a better conference, but this will also support our

scholarship fund.

31

For more details on tickets, sponsorships or community involvement, you'll find the

Stencon website in the show notes or counting on you.

32

OK, on to the show

33

Mi capretto, bienvenido a Learning Basics Statistics.

34

Hello Alex, muchas gracias.

35

Thank you.

36

Yeah, thanks a lot for taking the time.

37

That's actually a bit weird to talk to you in Spanish in English now because we're still

talking Spanish.

38

Yeah.

39

for the benefit of the world, we're gonna do that in English.

40

So it's awesome.

41

I'm really happy to have you on the show because with

42

You started as a colleague with the gears now.

43

You're definitely a friend, or at least I consider you a friend.

44

I will tell you after the recording if I consider you friend, depending on how it goes.

45

That's smart move, smart move.

46

I've lost quite a few friends because of my editing skills.

47

Yeah, so I mean, it's a long

48

interview I've had a lot of people who say you should have Tomica Preto on the show and I

always answered yeah I'll come to the show very soon don't worry we're finishing working

49

on a project right now together so well I'll invite him at that point so that he can talk

about the project and you guys maybe know what the project is about but you'll see at

50

at the middle of the episode, more or less, people.

51

But I mean, if you listen to the show regularly, you know which project I'm talking about.

52

But first, Tommy, we'll talk a bit about you.

53

Yeah, basically, can you tell people what you're doing nowadays?

54

You know, and yeah, like, what do your days look like?

55

So I'm doing quite a lot of things regularly.

56

Mainly, I work at Pimes Loves with a great team.

57

We work in very interesting projects doing basin stats.

58

I have the pleasure to be working with the people making the tool that I love.

59

That's amazing.

60

It's also great, I don't know, when we are working on a project, we realize time -seen is

to be able to do something or there's something broken.

61

We are not wondering, is this going to be fixed at some point in time?

62

Are the developers working on it?

63

We can just go and change the things.

64

Well, we have to be responsible because otherwise the community will hate us

65

changing the things all the time, but I definitely really like it.

66

So I work at Pimesy Labs, it's my main job.

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I've been at Labs for around three years, I think.

68

In parallel, I also teach in university here in Argentina.

69

I live in Rosario, Argentina, which is like the third largest city.

70

in the country.

71

After, so far, these nerds don't know Argentina.

72

We'll see if I know Argentina in well enough after Buenos Aires, of course, and Córdoba.

73

Yeah, I think that's the correct order.

74

And of course, for the football fans, the city of Angel Di Maria and Lyon ABC, of course.

75

Yeah, correct.

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And for some niche fans of football,

77

Like if you are from the UK or from some very particular area of Spain Also Marcelo

Gielsa, which is a coach A very particular coach He is also from I didn't know he was from

78

Rosario too, ok Yeah, yeah, yeah, we have very particular characters in the city Yeah, now

I understand why he's called El Loco Ok, ok Yeah, yeah That's how we call Tommy inside

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Pimcy Labs You are not supposed to tell that to people yeah, right, ooh, I'm sorry

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I'm not supposed to say a lie to you.

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On the show I can't.

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Yeah, and so yeah, I live here in Rosario.

83

In Rosario, I don't know why I'm telling that in English.

84

I teach in our national university.

85

There's a program in statistics, which is a program where I studied.

86

Now I'm teaching also based in statistics.

87

There's a whole course

88

dedicated to Basin Statistics in the final year of the career.

89

It's a new course.

90

It started in 2023.

91

That was the first edition.

92

Now we are finishing the second edition.

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The students hate and love us at the same time because we make them work a lot, but at the

end of the day they learn or at least that's what they say.

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what we find in the things that they present.

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So yeah, those are my two main activities today.

96

I'm also an open source developer contributing mainly to Bambi, Pimc, Arvies.

97

Sometimes I am creating a random repository to play with something or some educational

tool.

98

Yeah.

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And from time to time I teach courses.

100

I've just finalized teaching a Python course.

101

But yeah, it's like a mixture between statistics, computers, basic statistics, Python,

also R, which was my first language.

102

And yeah, that's the world we're living in.

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Yeah, yeah, definitely.

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You do a lot of things for sure.

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Yeah, I think we can go in different directions, but I'm actually curious if you can talk

about...

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I know you have an exercise in your class where you teach patient base and stance, and you

introduce them with &Ms.

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yes!

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Can you talk a bit about that exercise on the show?

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I think it will be interesting for our listeners.

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yeah, yeah, definitely.

111

To be completely honest and fair, is not our idea.

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I mean, it's an idea that was actually published on a paper.

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I don't remember the name of the paper, but I'm gonna find it.

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I have it.

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And I'm gonna give you the real source of the game.

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But we have adapted that.

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Basically, the first day you enter

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a base in classroom, the teachers present you a problem saying, hey, something happened

with MMMs.

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In our case, we used the local version, which are called Rocklets.

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It's basically the same.

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It's chocolate, different colors.

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And we tell them, hey, the owner of the factory suspects that there's something happening

with the machine that creates

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the MMMs of a particular color and you need to figure out what's happening.

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And so we give them, so we divide the students in groups, we give them a bag to the

different groups and they have to open the bag, they have to count the number of pieces

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that they have of the different colors.

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At that point, the students realize that what they care about is whether it

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that particular color or not and the idea is to start thinking like in a statistical plus

basin way like what is the quantity we are trying to estimate or what is the quantity that

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will tell us the answer and then you say okay we are talking about a proportion all right

and do we know anything about that proportion?

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Well, it's a proportion.

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It can be between 0 and 1.

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It's a continuous quantity.

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And then, okay, we are going to work manually, so let's discretize that proportion.

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And we have 11 values from 0 to 1.

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And then, okay, what else do we know about that proportion?

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Are all the values equally likely?

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And you can notice that we are starting to build a prior.

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And students are like, no, we have five colors.

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The probability of this color being present 80 % of the time is not the same as the

probability of this color being present 20 % of the time, for example.

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And so we start like in a very manual way to build a probability distribution, which is

the prior for the proportion of items that are of that

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And then we say, okay, what's the kind of the data that we are collecting?

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And we end up saying, okay, this is a binomial experiment.

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And we talk about the different assumptions, independence, constant probability.

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And then, okay, how can we combine this information together?

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And we naturally talk about the Bayesian theorem.

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And yeah, we do all the math by hand with very simple numbers, but in a very intuitive way

with a problem that is interesting for students because they know those chocolates, they

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can feel it makes sense to put what they know about the problem into a probability

distribution.

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because they know that they know something about the problem.

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And doing some very simple math using probability rules that they already know, we can

arrive a solution in a basic way.

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And the end of that lesson is, okay, everything we did so far is what we are going to do

in this course.

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Like we are going to learn more about this approach to do statistics.

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And yeah.

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In the very end, they can eat the data, basically.

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And that's really interesting.

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In the very first edition, we used Rocklets, which are like &M's.

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And in the second edition, we used Gummy Bears.

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But the logic was more or less the same, but we changed the product.

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And I don't know what you're going to do in the next edition, but it will have some

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involved.

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It's definitely very interesting and I'm fascinated by these approaches to introduce stats

to people which are more intuitive.

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The student is involved in the problem from the very beginning.

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You don't start with a list of 10 abstract concepts

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Perhaps they know how to follow, but it's less attractive.

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So yeah, we do that and I really like that approach.

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Yeah, yeah.

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I mean, that's definitely super fun.

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That's why I want you to do that on the show.

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think it's a great way to stance and we'll definitely add that to the show notes as you

were saying.

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And for next year, well, I think you definitely should do that with Alpha Chores.

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Let's see if we have the budget to do that.

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Yeah, it's gonna be a bit more budget, yeah for sure.

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mean, the best would be with empanadas, but that should be not very...

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that shouldn't be very easy to do, you know, like the empanada can break.

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Nah, it's gonna be a whole mess.

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you know...

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Yeah, I know, and that usually happens like early in the morning, so the students will be

like, what are we doing here?

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Yeah, it's

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It's a nice confusion because it creates a nice, an impact.

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Like they enter the classroom and instead of having people saying, Hey, this is my name.

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we are going to work on that.

180

It's like, Hey, you have this problem.

181

Take some gummy bears.

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And they're like, what?

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What's happening?

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So that's, it's attractive.

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Yeah.

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Yeah.

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No, for sure.

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most of your students are like, do they already know about stance?

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Yes.

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you're teaching them the Beijing way?

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Yeah, yeah, so at that point...

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What's their most, you know, what's the most confusing part to them?

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How do they react to that new framework?

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I would say in general, we had good experiences, especially at the end of the journey.

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But in the very beginning, so when they start the course...

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They already have like 20 courses, let's say 15 because other courses are focused on

mathematics or programming, but they already have like 15 courses about statistics, but

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they are all about the non -basin approach.

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So frequentist approach.

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They know a lot about maximum likelihood estimation and all the properties.

200

At that point, they already spent hours writing mathematical formulas and demonstrating

results and all that.

201

But they are very new to Bayesian statistics, because all they know about Bayes is Bayes

rules.

202

That's the only thing they know.

203

And they also know there's an estimation method called the Bayesian method, but

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they are not using that at that point.

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And one thing that there may be other things, but one thing that takes some time for them

to adapt is, okay, parameters are not fixed anymore.

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And I put a probability distribution on top of that because in all the courses they took

before our course,

207

there's a lot of emphasis on how to interpret confidence intervals, p -values and

classical statistics.

208

At that point, they are not the typical student that is confused about interpreting

confidence intervals, p -values and frequency stats because they practice that a lot.

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But then it's hard for them to switch from parameters are fixed

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our interval either contains the parameter or not, but we don't know it, to, parameters

are random quantities and we put probability distributions on top of them.

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So there's a cost there, which is not huge.

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And what was really nice for us, Monte Carlo is something that really helped us from very

early we start

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computing quantities of interest with Monte Carlo, when they realize the power in that

approach, they're like, I really like this.

214

Because I have a probability distribution and I'm interested in this particular

probability, or I'm interested in a probability involving two random variables, or in many

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things.

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Once they discover how powerful that approach

217

They're like, this is really nice.

218

But yeah, it's a challenge, but I really like it.

219

And I think at the end of the day, they also like it and they see the power in the

approach.

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In fact, I have a student that's right now working on a Google Summer of Code project with

Bambi.

221

So it's based in stats.

222

And it seems I'm going to have another student working on a hierarchical model for his

223

So yeah, it's really nice.

224

Nice, yeah, yeah, for sure.

225

Who is the...

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So I know also, I think if I remember correctly, there is...

227

So you know Gabriel, who works on BEMI.

228

I don't remember his last name right now, do you?

229

It's hard, it's Gabriel Stech -Schulte.

230

I don't know...

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yes, something like that.

232

So sorry, Gabriel.

233

But Gabriel is also a patron of the show.

234

of Learn Based Stats, so he's really in the Bayesian state of mind.

235

Thank you so much Gabriel for all the support to Learn Based Stats, but also, and even

more importantly, the work you do on Bambi.

236

I know you've helped me a few months ago on a PR for HSGP, where I was testing Bambi's

HSGP capabilities to the limit.

237

Thank you so much, Gabriel and Tony, of course, for developing Bambi all the time and

pushing the boundaries on that.

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I know Gabriel.

239

So he was working in the industry and now he's back to academia, but in a more research

role.

240

And sorry, Gabriel, I don't remember all the details about this, but I do remember he was

doing something very cool, applying Basin stats.

241

So I'm like nudging

242

publicly to someday tell the world about what he does.

243

Because I remember being like, this is quite interesting.

244

So yeah.

245

Definitely.

246

Yeah, for sure.

247

Yeah.

248

Actually, let's talk about Bambi.

249

I think it's going to be very interesting to listeners.

250

So yeah, can you tell us what Bambi is about basically and why would people

251

use it.

252

The way I usually do that is at least people know or I tell them it's like BRMS in Python.

253

If you're interested in BRMS and don't know what that is, I think it's episode 35 with

Paul Berkner, he was on the show, I put that in the show notes.

254

But if you want now Tommy's definition of Bambi, so one of the main core devs of Bambi,

well here it is folks.

255

To be honest, your definition was already really good because it's one of the definitions

I usually give when I know the other party knows about VRMS.

256

basically, if you don't know R, I can tell you like in 30 seconds, R has a very particular

syntax to specify regression models.

257

where you basically say, okay, this is my outcome variable, use a symbol, which is a

tilde, and you say, these are my predictors.

258

And you pass that to a function together with a data frame, which is a very convenient

structure.

259

And that function knows how to map the names of the predictors to parameters and variables

in the model.

260

It knows how to take a model formula

261

and a data frame and some other information that's not always needed, and it constructs a

model with that information.

262

So that's like very built in into R.

263

Like if you go back to, I think to the S language, the formula syntax already existed.

264

Then the R language has the formula syntax in the base packages.

265

And a lot of packages built by people in R use the formula syntax to specify regression

models.

266

And a lot of people also extended the formula syntax to account for other things, like one

extension that we incorporated in Bambi is the syntax to have what in frequency stats you

267

call random effects.

268

that appeared I think the first time in the LME4 package which is a very popular package

in R to work with mixed effects model which is another name for hierarchical models it's

269

crazy how many names you have for that so basically in R you have this formula syntax and

this very short way of writing a statistical model

270

and lot of people created a lot of packages to have a larger variety of models.

271

Then go to Python.

272

Let's go to Python.

273

Python is a more general programming language.

274

It has great support for statistics, machine learning, basic stats, and all that.

275

But you don't have something like a model formula built in the language.

276

I think one of the very first attempts to build that, which was extremely successful, it's

Patsy, which is a library developed by...

277

I don't remember the name of the guy, sorry.

278

I think it's Nathaniel, but I don't remember the last name.

279

But that's like...

280

As far as I know, the first package and the largest package that brought the model

formulas to Python, and then other libraries started to build on top of that Patsy

281

library.

282

For example, stats models.

283

And stats models allows you not to copy and paste your R code, but basically to say, this

is in R how I will create a linear regression model.

284

Okay, in Python, what do I need to

285

Okay, I need a pandas data frame, model formula that it passed in a string and it works

the same way.

286

And so as it happened in R with people creating packages to extend those capabilities, the

same happened in Python.

287

Like you have stats models, which is very popular, but there are also many other

libraries.

288

And one of those libraries is Bambi, which extends

289

the model formula and uses the model formula in a basin context.

290

BAMB is stands for basin model building interface.

291

It uses a model formula and a syntax very similar to the syntax that you find in R to

create basin models.

292

I think what's great about it is that you're not only creating the model, but you also

have lot of functionalities to work with the model.

293

For example, obtain predictions, which is not trivial in many cases, or compute some

summary of interest, or help you to find prayers that are sensible for the problem that

294

you have.

295

And so yeah, I joined.

296

the Bambi project, I think it was in 2020 or 2021, while working with Osvaldo, he was my

director in Conicet, which is like a national institute for science and technology here in

297

Argentina.

298

yeah, and I really liked the interface and I saw many points that could be

299

improved, mainly that Bambi didn't support the syntax for random effects.

300

Actually, no Python library supported that because Patsy didn't support that.

301

And at that point in time, I was learning about programming languages and I was like,

well, maybe it's time to write a parser for model formulas.

302

And that's what I did.

303

And that was my first big contribution to Bambi.

304

And then we started to add, I don't know, more model families.

305

So Bambi now supports many more likelihood functions.

306

We started to add better default priors because the goal of these libraries is to allow

you to

307

a quick iteration.

308

It's not that we are rooting for, you should all use default priors and automatic priors.

309

No, please don't do that.

310

But if you want to have something quick and iterate quick, then that's not a bad idea.

311

Once you more or less have like a more refined idea of your model, then you can sit down

and say, okay, let's really think really.

312

about the priors.

313

So to summarize Bambi is a package built on top of PyMC.

314

I didn't mention that before.

315

That allows people to write, fit and work with base models in Python without having to

write a model in a probabilistic programming language.

316

There's a trade -off.

317

Like you can write a very complex model in two or three lines of code.

318

If you want full flexibility, you should use a PIMC.

319

And to conclude, said BAMBEE is the BRMS of Python.

320

We always take like BRMS as an inspiration and also as

321

Yeah, what we want to have in many cases because implementing Bambi, I learned a lot about

BRMS and how great it is actually because the complexities it can handle and the variety

322

of models and kind of things you can have in a model in BRMS is huge.

323

I mean, I'm not aware of any other interface like this that supports

324

as many things, base and non -base.

325

I mean, it's really amazing.

326

And yeah, we are always taking ideas from VRMS.

327

Yeah, Yeah, great, Samari.

328

Thanks to me.

329

And even like, brief history of Bambi, I love that.

330

So in the show notes, I added the link to

331

that you mentioned and also the link to the very first Learn Bay Stats episode which was

with Osvaldo Maldini.

332

So it was episode number one.

333

It's definitely a vintage one, people.

334

Feel free to...

335

I have a fun story about that.

336

yeah?

337

I don't know if I told you about this story but when Osvaldo recorded that...

338

think I know.

339

Yeah, you know, know.

340

When Osvaldo...

341

but I don't know if the public know

342

knows about that story.

343

So Osvaldo and I used to work like in the same building, not in the exact same office, but

his office was in front of my office.

344

So if he was talking to someone, I could listen.

345

Not very clearly, but I could realize he was talking.

346

And some random day I was in the office and I noticed that he was talking English, but

alone.

347

Like, not with another person.

348

And I said, what is he doing?

349

And then after that, he told me, yes, I was interviewed in a podcast that this other guy

who's been contributing to Arby's is starting.

350

And yeah, I think it's very cool.

351

I think it went very well.

352

And at that point in time, I didn't know you, but I knew there was a podcast guy and it

turns out that I witnessed

353

the first recording of Learned Basics Statistics, which is pretty fun.

354

And look where we are now.

355

Pretty interesting.

356

Yeah, this is really cool.

357

I love that story.

358

It was already all linked together.

359

I love that.

360

Yeah.

361

Yeah.

362

I really love Bendy for what you said, you just said, I think.

363

It's a great way to start and iterate very fast on the model.

364

And then if you validate the concept, then you can switch to PIMC and build the model

again, but then build on top of that.

365

And that's going to make all your modeling workflow way faster.

366

Yeah.

367

really love that.

368

Another thing also that's really good is for teaching, especially beginners,

369

that will abstract away a lot of the choices that need to be made in the model.

370

As you were saying, it's not necessarily what you want to do all the time, but at least to

start with, you know, it's like when you start learning a new sport.

371

Yes, there are tons of nuances to learn, but, you know, if you focus on one or two things,

you already have the Pareto effect.

372

Well, then Bambi allows you to do that, and I think that's extremely valuable.

373

Yeah, and another point I'm realizing I forgot to mention is that it lowers the the

entrance barrier.

374

Like, there are a lot of people who are not statisticians, but they do stats because they

have experiments or they have they are studying something and they have data and they have

375

some level of familiarity with some models and they know that that's the model they want

to fit.

376

But probably writing PIMC

377

and working with indexes and demons and quarts is too much and going to Stan and typing

everything is also too much and they don't work with R and they want some higher level

378

interface to work with, then Bambi is also what they use.

379

And yeah, I also really like that.

380

It makes basic stats

381

more welcoming for people that are not experts at writing code, which is completely fine.

382

Because a lot of people out there are trying to solve already difficult problems and

adding the extra complexity of being an expert in a PPL maybe too much.

383

So that's also another reason to have these interfaces.

384

Yeah, yeah, yeah.

385

I definitely completely agree.

386

I that's also...

387

So basically, if people are curious about Bambi and get started with that, I definitely

recommend taking a look at the Bambi's website that I put in the show notes.

388

also, well, probably then about our new course, Tommy, that's the project that was in the

notes.

389

So this is all I am happy to have you on the show here, please.

390

So the course is called Advanced Regression with Bambi and Pimc.

391

Precisely, it's on the intuitive -based website, so of course I put that in the show notes

for people who want to take a look at it.

392

If you're a patron of the show, have 10 % off.

393

This is the only discount that we do, so I hope you appreciate it.

394

That's how special you are.

395

Thank you so much, patrons.

396

And yeah, maybe Tommy tell us about, you know, the course and what it is about and for

whom in particular that would be.

397

We spent a lot of time on this course.

398

It took us two years to develop.

399

So, yeah, I'm super happy about it.

400

I'm also super happy that it's done.

401

But yeah, maybe give us the elevator pitch for the course who that before.

402

and why would people even care about it?

403

So the Advanced Regression Course is a very interesting course with a lot of material,

with a lot of very well thought material, which in all cases went through a lot of

404

reviews.

405

As the title says, it's a course about regression, but also as the title says,

406

it's an advanced regression course.

407

It doesn't mean it starts from the beginning being extremely advanced and it doesn't mean

it involves the craziest mathematical formulas that you're going to see in your life, but

408

it means it's the course you have to take if you want to give, sorry, if you want to take

that second or third step in your learning journey.

409

Like for example, if you took an introductory course like yours or another introductory

course and you feel that's not enough or you are open to learn more, you are eager to

410

learn more, then that's the course for you.

411

Of course, it has a base in approach and it uses a lot of Python, Bambi and Pimc.

412

Every time I talk about regression, I want to qualify something.

413

I remember a conversation I had with colleagues when I was just starting in a previous

job.

414

They were telling me they were taking a course about statistics, like those courses where

you have a ton of topics, but only very lightly colored.

415

And they were like, yeah, the first two units is regression.

416

And this is a lot.

417

And I was telling them, in university, I had six courses about regression.

418

It was not just two units in a course.

419

And that's because I think in many cases, people think that regression is something very

simple.

420

It's the linear regression that you learn in

421

basic statistics course, like you have a predictor and you have an outcome variable and

you have a predictor, then that's simple linear regression.

422

You have multiple predictors, you have multiple linear regression.

423

And that's it.

424

That's all linear regression gives you.

425

And all the rest are crazier things that fall under the machine learning umbrella.

426

But in the course, we see that that's

427

the whole story.

428

So many things are regressions or if you don't like the term maybe we can give you a

better term in the future but so many things are linear models which sounds pretty basic

429

right?

430

You say this is a linear model this is a linear equation it's like this is for dummies but

if you're curious take the course and and you will see

431

With linear models, you can do a lot of crazy things.

432

Of course, we start with simple linear regression and we do multiple linear regression.

433

But then very quickly, go to logistic regression, Poisson regression, we talk about

categorical regression, multinomial regression, when your outcome is categories and you

434

have multiple categories.

435

And then it goes crazy.

436

and we have zero inflation and we have overdispersion and we finalize the course talking

about hierarchical models in the context of regressions and it ends with a very

437

interesting model that you developed.

438

So the course is very complete, it starts

439

A few things that we assume people know but we like review them.

440

But then very soon we start covering new things.

441

I think in all cases we show how to do things with Bambi and how to do them with Pine T.

442

We have a lot of visualizations.

443

Our editor did an amazing job at editing the video so we also have animations and all

that.

444

Yeah, it's a product I'm proud of.

445

Yeah, it's nice.

446

Yeah, definitely.

447

There is so much that we've done, in this foreign territory.

448

Well, I learned so much because...

449

Me too.

450

Yeah, as you were saying, it sounds like what a regression is, just something from the

past.

451

But it's actually used all the time.

452

You know, even the big LMs now, in the end, it's a lot of dot products and dot products

are matrices multiplied with vectors and, you know, a linear regression is actually not

453

that far from that.

454

It's actually exactly that.

455

So if you learn and understand really the nitty gritty of hard regressions, complex

456

you already know a lot of things you're going to need to to need.

457

You're going to need to know when doing Bayesian modeling in the trenches.

458

That's, that's for sure.

459

And that's also why I learned so much in this course, because I had to really dig into the

regression models.

460

And, we show you how to do that from simple regression to binomial regression.

461

Poisson regression, stuff you guys obviously at least have heard about, but then we teach

you more niche and advanced concepts like zero inflated regressions, over dispersed

462

regression, which is one of the chapters you worked on, Tommy, and you folks are gonna

learn a lot on that, like not only how to do the models, but then what to do with the

463

models after.

464

how to diagnose them, how to become confident about the model's predictions.

465

And also we teach you about a personal favorite of mine, which is the categorical and

multinomial regressions, which I use a lot for electoral forecasting.

466

But also you're going to use them a lot, for instance, for any more than two categories,

you're going to use a multinomial or a categorical.

467

And that's just extremely important to know about them because they are not trivial.

468

There are lot of subtleties and difficulties and we show you how to handle that.

469

I that's personally, I learned so much.

470

Something I really loved is what you did in in the over dispersed lesson, you know, where

you were diagnosing the over dispersion and coming up with a bunch

471

custom plots to show that the model is under dispersed.

472

Yeah, that's a term.

473

Compared to the data.

474

And also then coming up with a test statistic, a custom test statistic to actually see

whether the model is under dispersed or not.

475

And I think that's really powerful because that shows you also that in the invasion

framework, I often get that question from beginners.

476

can I compute

477

test statistics, because that's a magic one in the fragrances framework.

478

I'm like, yeah, sure.

479

But you can also invent your own test statistics for your own purpose here.

480

You don't have to use a pre -baked test statistic.

481

You have posterior samples.

482

can do whatever you want with them.

483

I thought that was like, that's definitely one of my favorite parts of the course.

484

And something I realized we forgot to mention, and I really like,

485

about the course and I really like having that in the course is all the different parts

where we talk about parameter identifiability and overparameterization and it's like we

486

don't tell you, take this outcome, take these three predictors and put them into the

machine and you're good to go.

487

I think that's probably, that will be a difficult part the first time you encounter

488

in the course, but we cover it multiple times in multiple lessons.

489

And the reason is it's a very important topic that's covered in many places, but I think

with not enough emphasis.

490

So we did our best to include that topic in many lessons to show it from different angles,

show how it can happen under

491

synchro stances, and that's something I'm really proud about.

492

How much time and effort we invested in non -identifiability, parameter redundancy, and

all that.

493

And the different approaches to deal with that, that's something I'm proud of.

494

I'm very happy we did that.

495

Yeah, definitely.

496

That's a very good point.

497

I think I finally understand overparameterization by working on this course because we see

it from, I think from lesson two or three, up until the last lesson, which is lesson nine.

498

Yes.

499

And we see it repeatedly.

500

And I think that's really good because it's a hard concept that's related to an

unidentifiability.

501

That happens a lot in models, not only Bayesian models, all the, like any statistical

model, but it's

502

mathematical thing.

503

And then it appears all the time in models.

504

And that's related to an identifiability, but it's hard to understand.

505

So you have to repeat it and really, really understand what that means.

506

then only then you can develop an intuition of what that really is and when it happens.

507

So yeah, definitely that's, that's also something I personally learned a lot and enjoyed a

lot in this.

508

in building this course.

509

Yeah, me too.

510

What would you say is your favorite part of all the curriculum right now and also what is

the part that was much more complicated than you anticipated?

511

Good question.

512

I don't know if this is a favorite part, but something I really like about the course is

how many visualizations we created.

513

Like in every model, we always created a visualization to explore the posterior, to plot

predictions, to do things like that.

514

I really like when you create a model and you don't just show two numbers, you make a

beautiful thing to communicate what you found.

515

That's something I really like.

516

definitely, my favorite parts are the more advanced parts, like starting perhaps in lesson

five, lesson six, when we talk about categorical regression, multinomial regression, and

517

then everything that happens after that.

518

Because I think that every lesson has many things to learn.

519

So I couldn't say, okay, this

520

the part I enjoy the most because I enjoy all of them but definitely the second half and

something that was difficult actually while working on the lesson about over dispersion I

521

looked through a lot of books, papers and all that and it was not easy at all to

522

many references, examples, datasets, very well worked examples from end end.

523

Honestly, I thought I would find a lot more, many more resources, and it was not that

easy.

524

I read papers

525

from 50 years ago.

526

Those scanned papers, like written in machines.

527

Yeah, that was harder than what I anticipated.

528

Crafting that lesson required a lot of reading, not only for the complexity, but also to

find resources that helped me build the lesson.

529

Yeah, definitely that

530

challenging and unanticipated.

531

Yeah, that lesson was hard, for sure.

532

that was difficult one.

533

Yeah, I mean, for me, I think my favorite part was really, as I was saying, Not learning,

but really getting to another level of understanding of an identifiability and of

534

parameterization.

535

And also, the next level in my understanding of the zero -sum normal distribution.

536

Because I had to use it a lot in the whole lesson.

537

And so, I mean, in the lessons, in all the lessons I'm teaching in this course, so three

of them, I'm using zero -sum normal.

538

So I had a really deep, deep...

539

And actually, that's something that, yeah, the students have said from the beta version

that

540

Yeah, it's very interesting to see how you solve one of the unidentifiability that can

happen in models.

541

So like, for instance, with multinomial models, one of the probabilities, like the last

category's probability is entirely determined by the n minus one previous categories.

542

So that's basically what an overparameterization is.

543

If you put the parameter

544

the end categories, then your model is overparameterized because the last category is

entirely determined once you know about the end minus one, the previous end minus ones.

545

And so there are at least two ways to solve that as we show in the course.

546

One of the classic ones, and it's the one that automatically implemented in BAMBi is

reference encoding.

547

So you take one of the categories and you consider that

548

is the reference in O and you fix it to an arbitrary number.

549

So fix that parameter to an arbitrary number.

550

Usually it's zero.

551

And then all the other categories, these parameters are in reference to that category.

552

So you could do that, but you can also do, and that's what we show you also in the course,

you can also say, well, instead of fixing one category to zero, I'm going to fix the

553

other categories to zero.

554

And that way you can still have n parameters, one for each category, which is really cool

because that way you don't have to think about one category as a reference.

555

And you just use a zero for normal distribution instead of normal distribution.

556

And that distribution is going to make sure that the sum of the categories sum to zero.

557

So that will depend when you prefer one or the

558

But usually when you don't have a natural placebo, you will probably prefer the zero

-subnormal parameterization because then there is no obvious reference.

559

Whereas a placebo is an obvious reference, you probably want all the parameters in

reference to that category.

560

But the zero -subnormal is going to be in reference to the average of all the categories.

561

And you can actually model an average for all the categories

562

this parameterization and then all the categories will be an offset of that baseline.

563

So that was definitely something super interesting that helped me pass the level in my

understanding of the distribution in that course.

564

And definitely a lot of better testers appreciated it.

565

I guess you want to say something also, but that's only because you know the zero sum

novel quite well.

566

Yeah, yeah.

567

But something like

568

Something nice I wanna say about the zero -sum normal.

569

In PyMC, the Serious or Normal is implemented as a distribution, which I think it would be

better if we could say, okay, this is a normal distribution plus transformation or a

570

restriction.

571

But having something called Serious or Normal and being able to use that as problem as any

other PyMC distribution is very convenient because the user doesn't have to deal with all

572

the details.

573

to get that constraint.

574

While if in PyMC you wanna have like other encoding, like you wanna have reference level,

you have to do it in a very manual way.

575

You have to create a vector of normals with shape n minus one.

576

Then you have to concatenate a serial to that other vector.

577

And then you get a new vector and that's vector you use in your model.

578

And you end up having like a constant in your trace and then Arvis complains about not

being able to compute our hat, for example, because they are all zeros or all constant.

579

And the zeros on normal is also like more appealing for the general users.

580

They just replace normal with zeros on normal.

581

and you're good to go.

582

That doesn't mean we shouldn't think about what we're doing.

583

I'm just talking about from like user experience, it's much easier to use a

SerialSumNormal and also more intuitive in most of the cases.

584

But yeah, I think the summary and how this relates to the course is think about parameter

restrictions that you add to the model.

585

think about how that changes the meaning of the parameters and then be responsible with

what you do.

586

But know that there's not a single recipe for solving that kind of problems.

587

Yeah, yeah.

588

Yeah, and that's also why we have the whole community in intuitive ways and we have the

discourse that people can ask questions because unfortunately there is no...

589

one size fits all.

590

I mean, I say unfortunately, that's actually pretty cool because otherwise, I guess what

we're doing would be pretty boring.

591

Time is running by and I think we've covered that topic quite well.

592

I I could talk about regression quite a long time, but I think that's a good overview.

593

And of course, if people are interested in some of the topics we talked about here,

594

Let me know and I can do a special episode about some parts of Regressions that you're

interested in or you're really wondering about.

595

Or we can even do a modern webinar showing you some things, some answers to the most

frequently asked questions you have about Regressions.

596

for sure, let us know about that.

597

And well, if we made you curious to take the course.

598

That's awesome.

599

I think this will be a lot of hours well invested.

600

Yeah, because it's nine lessons.

601

It's, I don't know how many hours of videos, but a lot.

602

You have lifetime access to that.

603

have exercises, which are very important.

604

Folks, I know I sound like a very old professor here, but actually I think the most

valuable of the course is not only watching the videos, but also doing the exercises.

605

and going through the solutions that you have all on the repo and asking questions on the

discourse, answering questions on the discourse, being part of that community.

606

Basically that's really how you're going to get the most out of yeah, like it's, you can

not learn how to ride a horse by just watching people riding horses.

607

It's the same with patient modeling.

608

If you just watch the videos, that will be entertaining for sure, but you're not gonna get

the most out of it.

609

So, yeah.

610

And if you do take the course, please say hi.

611

You are gonna be very happy to have you there and definitely wanna hear from you.

612

Tell me maybe, yeah, something I wanted to ask you before letting you go is, I know you've

done some work lately about sparse matrices.

613

If I remember correctly, in PyTentor, is that something you think would be useful here to

share a bit for listeners?

614

Yeah, yeah, can, I It's a topic I really like and I wish I knew more about that and always

like trying to learn.

615

Like there's some depth at which I know nothing about how that works.

616

But basically,

617

You already mentioned this, many things can be expressed as dot products.

618

And a subset of those many things can be expressed as a dot product between a matrix and a

vector.

619

That happens all the time in linear models.

620

That's basically the gist of linear model.

621

And in a subset of those cases, one

622

the matrix of that dot product is very sparse.

623

And if it's very sparse...

624

So define a sparse...

625

Yeah, define a closed matrix for example.

626

You have many entries in a matrix, but most of them, the great majority of them, are zero.

627

So it means in the multiplication they are not going to contribute anything to the final

628

If you do a dot product between a sparse matrix and a dense vector, dense is the opposite

of a sparse, meaning that you can have some zeros, but you don't have so many zeros to the

629

point where non -series are the rare value.

630

Anyway, if you have a big sparse matrix and a dense vector and you multiply them, you do a

dot product.

631

you're going to spend a lot of time computing things that are serial and will always be

serial and contribute nothing to the end result.

632

Of course there are, like, for a long time there have been structures to store these

special matrices in computers in such a way that you save space because

633

If you have a huge matrix with a lot of zeros stored in a dense way, that takes memory.

634

If you don't tell the computer those values are all the same, it doesn't know about that.

635

So it's going to take a lot of memory to store that matrix.

636

But with a sparse matrix, first you can save a lot of space into storage of the matrix.

637

And then you can exploit the sparsity to do less computations.

638

And at the end of the day, have computations that run faster.

639

And if you are doing MCMC, which means that you are evaluating the log P and its

derivative many, many times, it means you're multiplying.

640

If you're doing

641

matrix and vector multiplication a lot of times.

642

So gaining time, making that computation faster is something that we want to have.

643

yeah, PyTensor has some support for sparse matrices and sparse objects in general.

644

But as far as I know, that support comes from

645

old Tiano days.

646

There has been some maintenance, but not a lot of features have been added.

647

And yeah, for some projects at Labs, I've been writing my custom things to do dot products

between sparse matrices and dense vectors.

648

Unfortunately, I didn't have time yet to put that into PyTensor, but I want to do that

649

someone wants to collaborate on that endeavor, I'm more than happy.

650

But yeah, I think it's something that we should do more.

651

And the main motivation was that I wanted Bambi to do that by default, because Bambi is

doing the simple thing of multiplying big dense matrices.

652

when some of those matrices could have been sparse.

653

It's definitely not like new theory or new computational techniques, but it's taking

things that already exist and making them usable, first available and then usable for the

654

wider community.

655

And I don't know, I have fun doing those kinds of things.

656

Yeah, I mean, I think this is extremely valuable.

657

I hope you'll have time to include that in Python.

658

In a few weeks or months.

659

I mean, if I had time, but I definitely helped you, Matt.

660

Unfortunately, now with the new job and the other projects that have

661

to finish, like, don't have a lot of time for that.

662

yeah, but I mean, this is also definitely something that I want to learn more about

because it happens quite a lot.

663

And this is extremely frustrating.

664

Yeah, it's just like your brain, it feels weird because your brain when it sees a zero, it

knows if this term is not going to be useful.

665

So you can kind of get rid of it when you do the computation

666

You you do any computation by hand, you get rid of the zeros very easy.

667

But the computation does, the computer doesn't know that.

668

So you have to tell it because otherwise it spends a lot of time doing useless

computation.

669

And then in the end it's like, yeah, that's a zero.

670

But then you spent a lot of seconds doing that.

671

And that's stupid.

672

But you have to tell it, right?

673

It's what I tell with computers a lot, Computers are very powerful, but often they are

very dumb.

674

So you need to tell them exactly what you want.

675

And that's basically what you're trying to do here.

676

That's really interesting because that also happens very frequently, doesn't it?

677

Yeah, yeah.

678

For those who are curious about it and want to take a deeper dive, Daniel Simpson, he has

a very interesting blog.

679

And in that blog, he has many posts about doing things with sparse mentacies.

680

because I didn't mention this, but these matrices can have particular structures and if

they have that particular structure, can exploit some property of matrices and then do the

681

computation even faster.

682

like dot products, inverses, transposes, and things like that, determinants.

683

If you have matrices with particular structures, you can exploit those structures to save

684

and perhaps also memory.

685

And Daniel wrote a lot of posts doing things with sparse matrices using Jax, which, know,

PyTensor has these multiple backends.

686

It has a C backend, it has a Numba backend and a Jax backend.

687

And what has been frustrating to be honest is that the support for sparse matrices

688

varies a lot in those backends.

689

And that's one of the reasons that makes it harder to have something available that works

for most of the cases.

690

So in my use case, I implemented what I needed for the particular model that I had.

691

But if you want to have something public,

692

available for the wider community, it should work in more than just one single case.

693

But yeah, I think what's needed is a few people with some time to work on that and that

should be it because many things are already invented.

694

I'm not saying the task is trivial, not at all.

695

I'm saying it's...

696

It's about investing time, programming, designing, testing, and all that.

697

Yeah.

698

Yeah, so you heard it, folks.

699

Really, if you're interested in working on that, and you don't need to be an expert on

that because we have people like Tommy on the Pimesy repo who can mentor you.

700

If you're interested in that and you want to dive a bit into open source, please contact

me and I'll put you in contact

701

the appropriate authorities, as we say.

702

And yeah, so we should definitely put that blog post by Dan Simpson in the show notes,

Tommy, if you can do that.

703

also, is there anything you can share already in the show notes from your custom

implementation?

704

Yeah, I have all the repository that is public.

705

Perhaps I can update

706

with the latest things.

707

But I do have a few things to share.

708

Both implementations and experiments of myself testing those implementations.

709

Nice.

710

Which implementations are those?

711

In which cases could people use them?

712

Just Matrix.

713

If you write it, it's SPMB.

714

It's a sparse matrix.

715

SPMB, think.

716

But basically sparse matrix dense vector multiplication.

717

That's what I care about.

718

But that's in PyTensor.

719

PyTensor, C, Numba, JAX, many things.

720

But yeah, it's PyTensor with different backends.

721

Okay, so it would be like, for instance, you could use that function that's written in

PyTensor.

722

in a PyMC model.

723

Yeah, yeah, that's the goal and that's what I did in my use case.

724

Yeah, yeah, yeah.

725

It's like you have a sparse matrix multiplication somewhere in your PyMC model.

726

Instead of just doing pm .math .dot, you would use that custom...

727

Another function.

728

You would use that custom PyTensor function.

729

Correct.

730

Yeah, but the problem I was telling is, let's say you want to use the great new, not

PySum, okay, then you need a number backend to be

731

so you have that sparse thing implemented in Lumber and so on.

732

That definitely would awesome to have people help out on that.

733

I definitely love to that, unfortunately, I cannot extend my days.

734

That's really fascinating work.

735

That's really cool.

736

I'm hoping to have to do that at one point for work.

737

So you are forced to do it?

738

Yeah, either for the Marlins or for the Labs project.

739

Because then I'm forced to dive into and do it and probably do a PR to finally push that

to Pytancer Universe.

740

That's how a lot of my PRs end up being, you know.

741

That'd be great, I'd say.

742

I'd love that.

743

I love that because I've definitely been beaten by that before.

744

that's, yeah.

745

I had also looked into implementing a sparse Softmax implementation in Pytensor.

746

If I remember correctly, that didn't need to be very hard and I didn't have a lot of time

to work on that project, so I had to abandon it.

747

But yeah, definitely that'd be super fun.

748

Great, so, Tommy, it's already been a lot of time, maybe I just have one more question

before I go to last two questions.

749

Now I know you, learn a lot of stuff, we kind of work similarly so I think something I'd

like to ask you is what are you thinking about these days?

750

What do you want to learn in the coming week or coming month?

751

that's an interesting question.

752

I've been learning more about hierarchical models.

753

So it seems like, but shouldn't you already know about that topic?

754

Yeah, but turns out there are a lot of things to learn.

755

And so I've been learning about basic modeling and hierarchical models, like in multiple

ways, definitely gaining intuition through like computer exercises.

756

helped me a lot.

757

But lately, I went to more formal sources to have a look at the math and have a look at

the properties to better understand assumptions, consequences of those assumptions, trying

758

to understand when we can avoid computations.

759

In some point, my understanding was, okay, we have HMC.

760

This is the best thing in the world.

761

we pass any model between quotes because it's not any model but let's say any model and it

just works.

762

Okay, yes, you can have some problems but let's say it just works.

763

But then I've been learning more about those cases where you can avoid using such a

sampler or you can...

764

I know it sounds boring to write your own MCMC routine but if you have a A model

765

that you know very well and that's the model you want to use and nuts is going to take 30

hours because you have millions of parameters probably it's worth it like having a look at

766

the theory and realizing if you can do something more and I'm learning about that and I

really like it it's challenging I think that with

767

the experience of having worked a lot with BASIN models, is much easier to digest all

that.

768

So that's one of the things that I'm learning about.

769

Another thing that I'm always learning, and there's a book that we have been sharing

lately with folks at Labs and on Twitter.

770

The book is called, Richly Parametrized

771

linear models or something like that.

772

But something about models with a lot of parameters and how to work with those models.

773

And the book is great.

774

I enjoyed it.

775

And the topic is the connection between many different models that seem to be different,

but how they are connected to each other.

776

And I really enjoy that.

777

Like, you have a spline model.

778

You have a model with splines and then you have a hierarchical model but if you have these

particular priors and you go to the models that distribution it matches that other thing

779

and seeing those connections between the different models and modeling approaches is

really nice because it may seem boring at some point but that's how you

780

really grasp the depths of something.

781

So yeah, those are two things I'm learning about these days and I enjoy learning about

those things.

782

Yeah, I can tell you, you love learning about new things.

783

I do too, I think that's why also we work so well together.

784

And if you have a link to the book you just mentioned...

785

Yeah, I will share the book to edit.

786

I'm very bad at remembering exact names.

787

Fortunately, I can just search my computer so I know one or two words and then I can get

what I want.

788

That's cool.

789

sounds about right.

790

Well, Tommy, that's great.

791

I think it's time to call it a show.

792

We've got a lot of ground.

793

Of course, a ton of questions I

794

Still ask you, let's be respectful of your time.

795

But before, I'll let you go, of course.

796

I'm gonna ask you the last questions.

797

I'll ask you if you had guests at the end of the show.

798

you could...

799

No, sorry.

800

First one is if you had unlimited time and resources, which problem would you try to

solve?

801

I don't know if this problem has like a particular name, but you know, I enjoyed...

802

working with samples obtained with MCMC methods.

803

And it's really nice learning about how they work and how to diagnose them and all that.

804

But if we could have just a method that gives us real samples from any posterior

distribution that we work with, or we could have a very clever machine that knows the

805

details about every model

806

without us noticing, it uses a specific method to give us draws from the posterior,

meaning that you don't need to worry about divergences, convergence, and things like that,

807

where you can just focus in the analysis of the outcome.

808

I will work on that.

809

Because, and it's something I've been thinking more these days, like, now I need to wait

for the compilation.

810

and now I need to wait a few hours to get the draws.

811

If I could have something that saved me from that, even though I enjoy learning about how

it works and how to improve it depending on the kind of problems I'm having, yeah, I would

812

definitely like getting rid of MCMC and just do MC.

813

But I don't know if it's possible.

814

But if I'm here to dream, I'm going to have like...

815

Yeah, a very ambitious dream.

816

sure.

817

Yeah, Let's dream big.

818

Yeah, I agree with that.

819

Kind of having a...

820

Yeah, what I often dream about is having kind of like a Javi's like Iron Man.

821

I mean, like, can you try that version of the model?

822

Something like that.

823

that'd be fantastic.

824

Yeah.

825

Nice.

826

then second question.

827

If you could have dinner with any great scientific mind that alive or fictional, who would

it be?

828

And keep in mind that you cannot say myself because you already had dinner with me.

829

then we have to finish the recording.

830

Yeah, I I knew you were going to answer myself and I definitely appreciate that.

831

But you already had dinner with me, so you have to choose one of us.

832

Yeah,

833

Again, let me explain the answer.

834

I don't know why, but I'm a fan of movies and documentaries about World War II.

835

And one movie I enjoyed a lot and like I was really into the movie with a lot of attention

and very interested in what was happening was the, I think in English it is called the

836

Imitation Game, but in Spanish we call it...

837

the Enigma code or something like that.

838

And I really enjoyed that movie.

839

And I was fascinated seeing the machine moving the things and making noise, trying to

crack the machines to understand the message and then using like, okay, now we have the

840

information.

841

What do we do with that information?

842

So definitely...

843

I'm talking about Alan Turing and we have dinner with him to talk about everything.

844

How he was recruited, how they come with ideas, how they used it, what was hard about

making choices because it was both a technical problem but also a political, human

845

problem.

846

And then to talk about what happened after that.

847

So yeah, I think

848

The bad thing about that dinner would be that I would like it to last for many hours

because I would have many questions.

849

But yeah, that would be one person I would like to have dinner with to interview and ask a

lot of things.

850

Yeah, Great choice.

851

Fantastic choice.

852

Invite him at Christmas.

853

Christmas dinner

854

takes hours, so I think that's That's a very good opportunity.

855

Whether in France or Argentina, they always last hours, so you know.

856

That's good.

857

Awesome.

858

Well, thanks a That was a blast to finally have you on the show.

859

More than 100 episodes after you eavesdropped on Osvaldo's door at the Cunicet.

860

In Spanish, I think you would say, a little bit Quechua, and yeah, I'm sure.

861

yeah, yeah.

862

Yeah, that's great to have you on the show, And as usual, we'll put a link to your

website, to your socials, to a lot of links for those who want to dig deeper.

863

Thanks again, Tommy, for taking the time and being on this show.

864

Thank you, it was a lot of fun to be honest.

865

if Alex happens to invite you to the podcast, you have to say yes.

866

Thank you, Alex.

867

This has been another episode of Learning Bayesian Statistics.

868

Be sure to rate, review, and follow the show on your favorite podcatcher, and visit

learnbayestats .com for more resources about today's topics, as well as access to more

869

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870

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871

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872

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873

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874

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875

Alex Andorra.

876

can follow me on Twitter at Alex underscore Andorra, like the country.

877

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878

Thank you so much for listening and for your support.

879

You're truly a good Bayesian.

880

Change your predictions after taking information in.

881

And if you're thinking I'll be less than amazing, let's adjust those expectations.

882

Let me show you how to be a good Bayesian Change calculations after taking fresh data in

Those predictions that your brain is making Let's get them on a solid foundation