It’s been a while since I did an episode about sports analytics, right? And you know it’s a field I love, so… let’s do that! For this episode, I was happy to host Ehsan Bokhari, not only because he’s a first-hour listener of the podcast and spread the word about it whenever he can, but mainly because he knows baseball analytics very well! Currently Senior Director of Strategic Decision Making with the Houston Astros, he previously worked there as Senior Director of Player Evaluation and Director...
Oct 22, 2021•1 hr 13 min•Season 1Ep. 49
In episode 40, we already got a glimpse of how useful Bayesian stats are in the speech and communication sciences. To talk about the frontiers of this field (and, as it happens, about best practices to make beautiful plots and pictures), I invited TJ Mahr on the show. A speech pathologist turned data scientist, TJ earned his PhD in communication sciences and disorders in Madison, Wisconsin. On paper, he was studying speech development, word recognition and word learning in preschoolers, but over...
Oct 08, 2021•1 hr 1 min•Season 1Ep. 48
The field of physics has brought tremendous advances to modern Bayesian statistics, especially inspiring the current algorithms enabling all of us to enjoy the Bayesian power on our own laptops. I did receive some physicians already on the show, like Michael Betancourt in episode 6, but in my legendary ungratefulness I hadn’t dedicated a whole episode to talk about physics yet. Well that’s now taken care of, thanks to JJ Ruby. Apart from having really good tastes (he’s indeed a fan of this very ...
Sep 21, 2021•1 hr 16 min•Season 1Ep. 47
You wanna know something funny? A sentence from this episode became a meme. And people even made stickers out of it! Ok, that’s not true. But if someone could pull off something like that, it would surely be Chelsea Parlett-Pelleriti. Indeed, Chelsea’s research focuses on using statistics and machine learning on behavioral data, but her more general goal is to empower people to be able to do their own statistical analyses, through consulting, education, and, as you may have seen, stats memes on ...
Aug 30, 2021•1 hr 13 min•Season 1Ep. 46
As a podcaster, I discovered that there are guests for which the hardest is to know when to stop the conversation. They could talk for hours and that would make for at least 10 fantastic episodes. Frank Harrell is one of those guests. To me, our conversation was both fascinating — thanks to Frank’s expertise and the width and depth of topics we touched on — and frustrating — I still had a gazillion questions for him! But rest assured, we talked about intent to treat and randomization, proportion...
Aug 10, 2021•1 hr 9 min•Season 1Ep. 45
Episode sponsored by Paperpile: paperpile.com Get 20% off until December 31st with promo code GOODBAYESIAN21 Bonjour my dear Bayesians! Yes, it was bound to happen one day — and this day has finally come. Here is the first ever 100% French speaking ‘Learn Bayes Stats’ episode! Who is to blame, you ask? Well, who better than Rémi Louf? Rémi currently works as a senior data scientist at Ampersand, a big media marketing company in the US. He is the author and maintainer of several open source libra...
Jul 22, 2021•1 hr 15 min•Season 1Ep. 44
Episode sponsored by Paperpile: paperpile.com Get 20% off until December 31st with promo code GOODBAYESIAN21 I don’t know if you’ve heard, but there is a virus that took over most of the world in the past year? I haven’t dedicated any episode to Covid yet. First because research was moving a lot — and fast. And second because modeling Covid is very, very hard. But we know more about it now, so I thought it was a good time to pause and ponder — how does the virus circulate? How can we model it an...
Jul 08, 2021•1 hr 22 min•Season 1Ep. 43
Episode sponsored by Paperpile: paperpile.com Get 20% off until December 31st with promo code GOODBAYESIAN21 We often talk about applying Bayesian statistics on this podcast. But how do we teach them? What’s the best way to introduce them from a young age and make sure the skills students learn in the stats class are transferable? Well, lucky us, Mine Dogucu’s research tackles precisely those topics! An Assistant Professor of Teaching in the Department of Statistics at University of California I...
Jun 24, 2021•1 hr 6 min•Season 1Ep. 42
Let’s think Bayes, shall we? And who better to do that than the author of the well known book, Think Bayes — Allen Downey himself! Since the second edition was just released, the timing couldn’t be better! Allen is a professor at Olin College and the author of books related to software and data science, including Think Python , Think Bayes , and Think Complexity . His blog, Probably Overthinking It , features articles on Bayesian probability and statistics. He holds a Ph.D. from U.C. Berkeley, a...
Jun 14, 2021•1 hr 4 min•Season 1Ep. 41
We all know about these accidental discoveries — penicillin, the heating power of microwaves, or the famous (and delicious) tarte tatin. I don’t know why, but I just love serendipity. And, as you’ll hear, this episode is deliciously full of it… Thanks to Allison Hilger and Timo Roettger, we’ll discover the world of linguistics, how Bayesian stats are helpful there, and how Paul Bürkner’s BRMS package has been instrumental in this field. To my surprise — and perhaps yours — the speech and languag...
May 28, 2021•1 hr 6 min•Season 1Ep. 40
Episode sponsored by Tidelift: tidelift.com It’s been a while since we talked about biostatistics and bioinformatics on this podcast, so I thought it could be interesting to talk to Jacki Buros — and that was a very good idea! She’ll walk us through examples of Bayesian models she uses to, for instance, work on biomarker discovery for cancer immunotherapies. She’ll also introduce you to survival models — their usefulness, their powers and their challenges. Interestingly, all of this will highlig...
May 14, 2021•1 hr•Season 1Ep. 39
Episode sponsored by Tidelift: tidelift.com Imagine me rapping: "Let me show you how to be a good Bayesian. Change your predictions after taking information in, and if you’re thinking I’ll be less than amazing, let’s adjust those expectations!" What?? Nah, you’re right, I’m not as good as Baba Brinkman. Actually, the best to perform « Good Bayesian » live on the podcast would just be to invite him for an episode… Wait, isn’t that what I did??? Well indeed! For this episode, I had the g...
Apr 30, 2021•1 hr 28 min•Season 1Ep. 38
Episode sponsored by Tidelift: tidelift.com I don’t know about you, but the notion of time is really intriguing to me: it’s a purely artificial notion; we humans invented it — as an experiment, I asked my cat what time it was one day; needless to say it wasn’t very conclusive… And yet, the notion of time is so central to our lives — our work, leisures and projects depend on it. So much so that time series predictions represent a big part of the statistics and machine learning world. And to talk ...
Apr 16, 2021•1 hr 6 min•Season 1Ep. 37
Episode sponsored by Tidelift: tidelift.com I bet you already heard of Bayesian nonparametric models, at least on this very podcast. We already talked about Dirichlet Processes with Karin Knudson on episode 4, and then about Gaussian Processes with Elizaveta Semenova on episode 21. Now we’re gonna dive into the mathematical properties of these objects, to understand them better — because, as you may know, Bayesian nonparametrics are quite powerful but also very hard to fit! Along the way, you’ll...
Mar 30, 2021•1 hr 9 min•Season 1Ep. 36
Episode sponsored by Tidelift: tidelift.com One of the most common guest suggestions that you dear listeners make is… inviting Paul Bürkner on the show! Why? Because he’s a member of the Stan development team and he created BRMS, a popular R package to make and sample from Bayesian regression models using Stan. And, as I like you, I did invite Paul on the show and, well, that was a good call: we had an amazing conversation, spanning so many topics that I can’t list them all here! I asked him why...
Mar 12, 2021•1 hr 7 min•Season 1Ep. 35
Episode sponsored by Tidelift: tidelift.com We already mentioned multilevel regression and post-stratification (MRP, or Mister P) on this podcast, but we didn’t dedicate a full episode to explaining how it works, why it’s useful to deal with non-representative data, and what its limits are. Well, let’s do that now, shall we? To that end, I had the delight to talk with Lauren Kennedy! Lauren is a lecturer in Business Analytics at Monash University in Melbourne, Australia, where she develops new s...
Feb 25, 2021•1 hr 13 min•Season 1Ep. 34
How do people choose their career? How do they change jobs? How do they even change careers? These are important questions that we don’t have great answers to. But structured data about the dynamics of labor markets are starting to emerge, and that’s what Ben Zweig is modeling at Revelio Labs. An economist and data scientist, Ben is indeed the CEO of Revelio Labs, a data science company analyzing raw labor data contained in resumes, online profiles and job postings. In this episode, he’ll tell u...
Feb 12, 2021•58 min•Season 1Ep. 33
When explaining Bayesian statistics to people who don’t know anything about stats, I often say that MCMC is about walking many different paths in lots of parallel universes, and then counting what happened in all these universes. And in a sense, this whole podcast is dedicated to sampling the whole distribution of Bayesian practitioners. So, for this episode, I thought we’d take a break of pure, hard modeling and talk about how to get involved into Bayesian statistics and open-source development...
Jan 27, 2021•53 min•Season 1Ep. 32
I don’t know if you noticed, but I have a fondness for any topic related to decision-making under uncertainty — when it’s studied scientifically of course. Understanding how and why people make decisions when they don’t have all the facts is fascinating to me. That’s why I like electoral forecasting and I love cognitive sciences. So, for the first episode of 2021, I have a special treat: I had the great pleasure of hosting Michael Lee on the podcast! Yes, the Michael Lee who co-authored the book...
Jan 05, 2021•1 hr 9 min•Season 1Ep. 31
It’s funny how powerful symbols are, right? The Eiffel Tower makes you think of Paris, the Statue of Liberty is New-York, and the Trevi Fountain… is Rome of course! Just with one symbol, you can invoke multiple concepts and ideas. You probably know that symbols are omnipresent in mathematics — but did you know that they are also very important in statistics, especially probabilistic programming? Rest assured, I didn’t really know either… until I talked with Brandon Willard! Brandon is indeed a b...
Dec 18, 2020•1 hr•Season 1Ep. 30
I’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we? So, Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. You already heard his delightful Finnish accent on episode 20, with Andr...
Dec 02, 2020•1 hr 5 min•Season 1Ep. 29
In times of crisis, designing an efficient policy response is paramount. In case of natural disasters or pandemics, it can even determine the difference between life and death for a substantial number of people. But precisely, how do you design such policy responses, making sure that risks are optimally shared, people feel safe enough to reveal necessary information, and stakeholders commit to the policies? That’s where a field of economics, industrial organization (IO), can help, as Shosh Vasse...
Nov 20, 2020•1 hr 4 min•Season 1Ep. 28
In a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. You probably know a few forecasting models that try to predict what will happen on Election Day — who will get elected, by how much and with which coalition of States? But how do these statistical models work? How do you account for the different sources of uncertainty, be it polling errors, unexpected turnout or media eve...
Nov 01, 2020•1 hr 1 min•Season 1Ep. 27
I don’t know about you, but I’m starting to really miss traveling and just talking to people without having to think about masks, social distance and activating the covid tracking app on my phone. In the coming days, there is one event that, granted, won’t make all of that disappear, but will remind me how enriching it is to meet new people — this event is PyMCon, the first-ever conference about the PyMC ecosystem! To talk about the conference format, goals and program, I had the pleasure to hos...
Oct 24, 2020•46 min•Season 1Ep. 26
Have you watched the series « The English Game », on Netflix? Well, I think you should — it’s a fascinating dive into how football went from an aristocratic to a popular sport in the late 19th century England. Today it is so popular that it became a valuable business to do statistics on the game and its players! To talk about that, I invited Kevin Minkus on the show — he’s a data scientist and soccer fan living in Philadelphia. Kevin’s currently working at Monetate on ecommerce problem...
Oct 09, 2020•56 min•Season 1Ep. 25
Do you know what proteins are, what they do and why they are useful? Well, be prepared to be amazed! In this episode, Seth Axen will tell us about the fascinating world of protein structures and computational biology, and how his work of Bayesian modeler fits into that! Passionate about mathematics and statistics, Seth is finishing a PhD in bioinformatics at the Sali Lab of the University of California, San Francisco (UCSF). His research interests span the broad field of computational biology: u...
Sep 24, 2020•57 min•Season 1Ep. 24
If you’ve studied at a business school, you probably didn’t attend any Bayesian stats course there. Well this isn’t like that in every business schools! Elea McDonnel Feit does integrate Bayesian methods into her teaching at the business school of Drexel University, in Philadelphia, US. Elea is an Assistant Professor of Marketing at Drexel, and in this episode she’ll tell us which methods are the most useful in marketing analytics, and why. Indeed, Elea develops data analysis methods to in...
Sep 10, 2020•59 min•Season 1Ep. 23
If, like me, you’ve been stuck in a 40 square-meter apartment for two months, you’re going to be pretty jealous of Avi Bryant. Indeed, Avi lives on Galiano Island, Canada, not very far from Vancouver, surrounded by forest, overlooking the Salish Sea. In this natural and beautiful — although slightly deer-infested — spot, Avi runs The Gradient Retreat Center, a place where writers, makers, and code writers can take a week away from their regular lives and focus on creative work. But it’s no...
Aug 26, 2020•1 hr 7 min•Season 1Ep. 22
I bet you heard a lot about epidemiological compartmental models such as SIR in the last few months? But what are they exactly? And why are they so useful for epidemiological modeling? Elizaveta Semenova will tell you why in this episode, by walking us through the case study she recently wrote with the Stan team. She’ll also tell us how she used Gaussian Processes on spatio-temporal data, to study the spread of Malaria, or to fit dose-response curves in pharmaceutical tests. And fina...
Aug 13, 2020•1 hr 2 min•Season 1Ep. 21
Once upon a time, there was an enchanted book filled with hundreds of little plots, applied examples and linear regressions — the prettiest creature that was ever seen. Its authors were excessively fond of it, and its readers loved it even more. This magical book had a nice blue cover made for it, and everybody aptly called it « Regression and other Stories »! As every good fairy tale, this one had its share of villains — the traps where statistical methods fall and fail you; the terri...
Jul 30, 2020•1 hr 4 min•Season 1Ep. 20
