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Learning Bayesian Statistics — Technology
Learning Bayesian Statistics

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is?

Then this podcast is for you! You’ll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow.

When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible.

So I created “Learning Bayesian Statistics”, where you’ll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes — it’s also about failures, because that’s how we learn best.

So you’ll often hear the guests talking about what *didn’t* work in their projects, why, and how they overcame these challenges. Because, in the end, we’re all lifelong learners!

My name is Alex Andorra by the way. By day, I’m a Senior data scientist. By night, I don’t (yet) fight crime, but I’m an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love Nutella, but I don’t like talking about it – I prefer eating it.

So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you — just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!

Copyright: © Copyright Alexandre Andorra

Latest Episodes:

#28 Game Theory, Industrial Organization & Policy Design, with Shosh Vasserman Nov 20, 2020

Show notes

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 Vasserman will tell us in this episode. Shosh is an assistant professor of economics at the Stanford Graduate School of Business. Specialized in industrial organization, her interests span a number of policy settings, such as public procurement, pharmaceutical pricing and auto-insurance.

Her work leverages theory, empirics and modern computation (including the Stan software!) to better understand the equilibrium implications of policies and proposals involving information revelation, risk sharing and commitment.

In short, Shoshana uses theory and data to study how risk, commitment and information flows interplay with policy design. And she does a lot of this with… Bayesian models! Who said Bayes had no place in economics?

Prior to Stanford, Shoshana did her Bachelor’s in mathematics and economics at MIT, and then her PhD in economics at Harvard University.

This was a fascinating conversation where I learned a lot about Bayesian inference on large scale random utility logit models, socioeconomic network heterogeneity and pandemic policy response — and I’m sure you will too!

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

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

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

Links from the show:

Shosh's website: https://shoshanavasserman.com/
Shosh on Twitter: https://twitter.com/shoshievass
How do different reopening strategies balance health and employment: https://reopenmappingproject.com/
Aggregate random coefficients logit—a generative approach: http://modernstatisticalworkflow.blogspot.com/2017/03/aggregate-random-coefficients-logita.html
Voluntary Disclosure and Personalized Pricing: https://shoshanavasserman.com/files/2020/08/Voluntary-Disclosure-and-Personalized-Pricing.pdf
Socioeconomic Network Heterogeneity and Pandemic Policy Response:

#27 Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns Nov 01, 2020

Show notes

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 events? How do you model covariation between States? How do you even communicate the model’s results and afterwards assess its performance? To talk about all this, I had the pleasure to talk to Andrew Gelman and Merlin Heidemanns.

Andrew was already on episode 20, to talk about his recent book with Jennifer Hill and Aki Vehtari, “Regression and Other Stories”. He’s a professor of statistics and political science at Columbia University and works on a lot of topics, including: why campaign polls are so variable while elections are so predictable, the statistical challenges of estimating small effects, and methods for surveys and experimental design.

Merlin is a PhD student in Political Science at Columbia University, and he specializes in political methodology. Prior to his PhD, he did a Bachelor's in Political Science at the Freie Universität Berlin.

I hope you’ll enjoy this episode where we dove into the Bayesian model they helped develop for The Economist, and talked more generally about how to forecast elections with statistical methods, and even about the incentives the forecasting industry has as a whole.

Thank you to my Patrons for making this episode possible! Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

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

Links from the show:

Andrew's website: http://www.stat.columbia.edu/~gelman/
Andrew's blog: https://statmodeling.stat.columbia.edu/
Andrew on Twitter: https://twitter.com/statmodeling
Merlin's website: https://merlinheidemanns.github.io/website/
Merlin on Twitter: https://twitter.com/MHeidemanns
The Economist POTUS forecast: https://projects.economist.com/us-2020-forecast/president
How The Economist presidential forecast works: https://projects.economist.com/us-2020-forecast/president/how-this-works
GitHub repo of the Economist model: https://github.com/TheEconomist/us-potus-model
Information, incentives, and goals in election forecasts (Gelman, Hullman & Wlezien): http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdf
How to think about extremely...

#26 What you’ll learn & who you’ll meet at the PyMC Conference, with Ravin Kumar & Quan Nguyen Oct 24, 2020

Show notes

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 host Ravin Kumar and Quan Nguyen on the show.

Quan is a PhD student in computer science at Washington University in St Louis, USA, researching Bayesian machine learning and one of the PyMCon program committee chairs. He is also the author of several programming books on Python and scientific computing.

Ravin is a core contributor to Arviz and PyMC, and is leading the PyMCon conference. He holds a Bachelors in Mechanical Engineering and a Masters in Manufacturing Engineering. As a Principal Data Scientist he has used Bayesian Statistics to characterize and aid decision making at organizations like SpaceX and Sweetgreen. Ravin is also currently co-authoring a book with Ari Hartikainen, Osvaldo Martin, and Junpeng Lao on Bayesian Statistics due for release in February.

We talked about why they became involved in the conference, parsed through the numerous, amazing talks that are planned, and detailed who the keynote speakers will be… So, If you’re interested the link to register is in the show notes, and there are even two ways to get a free ticket: either by applying to a diversity scholarship, or by being a community partner, which is anyone or any organization working towards diversity and inclusion in tech — all links are in the show notes.

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

Links from the show:

PyMCon speakers: https://pymc-devs.github.io/pymcon/speakers
Register to PyMCon: https://www.eventbrite.com/e/pymcon-2020-tickets-121404065829
PyMCon Diversity Scholarship: https://bit.ly/2J3Vb9d
PyMCon Community Partner Form: https://bit.ly/35yq90L
PyMC3 -- Probabilistic Programming in Python: https://docs.pymc.io
Donate to PyMC3: https://numfocus.org/donate-to-pymc3
PyMC3 for enterprise: https://bit.ly/3jo9jq9
Ravin on Twitter: https://twitter.com/canyon289
Quan on the web: https://krisnguyen135.github.io/
Quan's author page: https://amzn.to/37JsB7r
Alex talks about polls on the "Local Maximum" podcast: https://bit.ly/3e1Ro7O
Support "Learning Bayesian Statistics" on Patreon: https://www.patreon.com/learnbayesstats

Thank you to my Patrons for making...

#25 Bayesian Stats in Football Analytics, with Kevin Minkus Oct 09, 2020

Show notes

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 problems, and prior to Monetate he worked on property and casualty insurance pricing.

He spends a lot of his spare time working on problems in football analytics and is a contributor at American Soccer Analysis, a website and podcast dedicated to… football made or played in the US (or “soccer”, as they say over there). Kevin is responsible for some of their data management and devops, and he recently wrote a guide to football analytics for the Major League Soccer’s website, entitled « Soccer Analytics 101 ».

To be honest, I had a great time talking for one hour about two of my passions — football and stats! Soooo, maybe 2020 isn’t that bad after all… Ow, and beyond football, Kevin is also into the digital humanities, web development, 3D animation, machine learning, and… the bassoon!

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

Links from the show:

Kevin on Twitter: https://twitter.com/kevinminkus
Kevin on GitHub: https://github.com/kcm30
Soccer Analytics 101: https://www.mlssoccer.com/soccer-analytics-guide/2020/soccer-analytics-101
American Soccer Analysis: https://www.americansocceranalysis.com/

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

#24 Bayesian Computational Biology in Julia, with Seth Axen Sep 24, 2020

Show notes

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: using computer science, mathematics, and statistics to understand biological systems. His current research focuses on inferring protein structural ensembles.

Open source development is also very dear to his heart, and indeed he contributes to many open source packages, especially in the Julia ecosystem. In particular, he develops and maintains ArviZ.jl, the Julia port of ArviZ, a platform-agnostic python package to visualize and diagnose your Bayesian models. Seth will tell us how he became involved in ArviZ.jl, what its strengths and weaknesses are, and how it fits into the Julia probabilistic programming landscape.

Ow, and as a bonus, you’ll discover why Seth is such a fan of automatic differentiation, aka « autodiff » — I actually wanted to edit this part out but Seth strongly insisted I kept it. Just kidding of course — or, am I… ?

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

Links from the show:

Seth website: http://sethaxen.com/
Seth on Twitter: https://twitter.com/sethaxen
Seth on GitHub: https://github.com/sethaxen
ArviZ.jl -- Exploratory analysis of Bayesian models in Julia: https://arviz-devs.github.io/ArviZ.jl/dev/
PyCon2020 -- Colin Carroll -- Getting started with automatic differentiation: https://www.youtube.com/watch?v=NG21KWZSiok

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

#23 Bayesian Stats in Business and Marketing Analytics, with Elea McDonnel Feit Sep 10, 2020

Show notes

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 inform marketing decisions, such as designing new products and planning advertising campaigns. Often faced with missing, unmatched or aggregated data, she uses MCMC sampling, hierarchical models and decision theory to decipher all this.

After an MS in Industrial Engineering at Lehigh University and a PhD in Marketing at the University of Michigan, Elea worked on product design at General Motors and was most recently the Executive Director of the Wharton Customer Analytics Initiative.

Thanks to all these experiences, Elea loves teaching marketing analytics and Bayesian and causal inference at all levels. She even wrote the book R for Marketing Research and Analytics with Chris Chapman, at Springer Press.

In summary, I think you’ll be pretty surprised by how Bayesian the world of marketing is…

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

Links from the show:

Elea's website: http://eleafeit.com/
R for Marketing Research and Analytics: http://r-marketing.r-forge.r-project.org/
Elea's Tutorials & Online Courses: http://eleafeit.com/teaching/
Elea on Twitter: https://twitter.com/eleafeit
Elea on GitHub: https://github.com/eleafeit
Tutorial on Conjoint Analysis in R: https://github.com/ksvanhorn/ART-Forum-2017-Stan-Tutorial
Test & Roll app: https://testandroll.shinyapps.io/testandroll/
Test & Roll Paper -- Profit-Maximizing A/B Tests: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3274875
Principal Stratification for Advertising Experiments: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3140631
CausalImpact R package: https://google.github.io/CausalImpact/CausalImpact.html
Chapter on Data Fusion in marketing: https://link.springer.com/referenceworkentry/10.1007/978-3-319-05542-8_9-1
Statistical Analysis with Missing Data (Little & Rubin): https://onlinelibrary.wiley.com/doi/book/10.1002/9781119013563
R-Ladies Philly YouTube channel:

#22 Eliciting Priors and Doing Bayesian Inference at Scale, with Avi Bryant Aug 26, 2020

Show notes

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 not only to envy him that I invited Avi on the show — it’s to talk about Bayesian inference in Scala, prior elicitation, how to deploy Bayesian methods at scale, and how to enable Bayesian inference for engineers.

While working at Stripe, Avi wrote Rainier, a Bayesian inference framework for Scala. Inference is based on variants of the Hamiltonian Monte Carlo sampler, and the implementation is similar to, and targets the same types of models as both Stan and PyMC3. As Avi says, depending on your background, you might think of Rainier as aspiring to be either "Stan, but on the JVM", or "TensorFlow, but for small data".

In this episode, Avi will tell us how Rainier came into life, how it fits into the probabilistic programming landscape, and what its main strengths and weaknesses are.

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

Links from the show:

Avi on Twitter: https://twitter.com/avibryant
Avi on GitHub: https://github.com/avibryant
Rainier -- Bayesian Inference in Scala: https://rainier.fit/
The Gradient Retreat: https://gradientretreat.com/
Facebook's Prophet: https://facebook.github.io/prophet/
BAyesian Model-Building Interface (Bambi) in Python: https://bambinos.github.io/bambi/
BRMS -- Bayesian regression models using Stan: https://paul-buerkner.github.io/brms/
Using Bayesian Decision Making to Optimize Supply Chains -- Thomas Wiecki & Ravin Kumar: https://twiecki.io/blog/2019/01/14/supply_chain/

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

#21 Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova Aug 13, 2020

Show notes

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 finally, she’ll tell us how she used Bayesian neural networks for drug toxicity prediction in her latest paper, and how Bayesian neural nets behave compared to classical neural nets. Ow, and you’ll also learn an interesting link between BNNs and Gaussian Processes…

I know: Liza works on _a lot_ of projects! But who is she? Well, she’s a postdoctorate in Bayesian Machine Learning at the pharmaceutical company AstraZeneca, in Cambridge, UK.

Elizaveta did her masters in theoretical mathematics in Moscow, Russia, and then worked in financial services as an actuary in various European countries. She then did a PhD in epidemiology at the University of Basel, Switzerland. This is where she got interested in health applications – be it epidemiology, global health or more small-scale biological questions. But she’ll tell you all that in the episode ;)

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

Links from the show:

Liza on Twitter: https://twitter.com/liza_p_semenova
Liza on GitHub: https://github.com/elizavetasemenova
Liza's blog: https://elizavetasemenova.github.io/blog/
A Bayesian neural network for toxicity prediction: https://www.biorxiv.org/content/10.1101/2020.04.28.065532v2
Bayesian Neural Networks for toxicity prediction -- Video presentation: https://www.youtube.com/watch?v=BCQ2oVlu_tY&t=751s
Bayesian workflow for disease transmission modeling in Stan: https://mc-stan.org/users/documentation/case-studies/boarding_school_case_study.html
Andrew Gelman's comments on the SIR case-study: https://statmodeling.stat.columbia.edu/2020/06/02/this-ones-important-bayesian-workflow-for-disease-transmission-modeling-in-stan/
Determining organ weight toxicity with Bayesian causal models: https://www.biorxiv.org/content/10.1101/754853v1
Material for Applied Machine Learning Days ("Embracing uncertainty"): https://github.com/elizavetasemenova/EmbracingUncertainty
Predicting Drug-Induced Liver Injury with Bayesian Machine Learning: https://pubs.acs.org/doi/abs/10.1021/acs.chemrestox.9b00264
Ordered Logistic Regression in Stan, PyMC3 and Turing: https://medium.com/@liza_p_semenova/ordered-logistic-regression-and-probabilistic-programming-502d8235ad3f
PyMCon website: https://pymc-devs.github.io/pymcon/
PyMCon Call For Proposal: https://pymc-devs.github.io/pymcon/cfp
PyMCon...

#20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari Jul 30, 2020

Show notes

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 terrible confounders, lurking in the dark; the ill-measured data that haunt your inferences! But once you defeat these monsters, you’ll be able to think about, build and interpret regression models.

This episode will be filled with stories — stories about linear regressions! Here to narrate these marvelous statistical adventures are Andrew Gelman, Jennifer Hill and Aki Vehtari — the authors of the brand new Regression and other Stories.

Andrew is a professor of statistics and political science at Columbia University. Jennifer is a professor of applied statistics at NYU. She develops methods to answer causal questions related to policy research and scientific development. Aki is an associate professor in computational probabilistic modeling at Aalto University, Finland.

In this episode, they tell us why they wrote this book, who it is for and they also give us their 10 tips to improve your regression modeling! We also talked about the limits of regression and about going to Mars…

Other good news: until October 31st 2020, you can go to http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020 and buy the book with a 20% discount by entering the promo code “GoodBayesian2020” upon checkout!

That way, you’ll make up your own stories before going to sleep and dream of a world where we can easily generalize from sample to population, and where multilevel regression with poststratification is a bliss…

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

Links from the show:

Regression and Other Stories on Cambridge Press website: http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020
Amazon page (because of VAT laws, in some regions ordering from Amazon can be cheaper than from the editor directly, even with the discount): https://www.amazon.com/Regression-Stories-Analytical-Methods-Research/dp/110702398X
Code, data and examples for the book: https://avehtari.github.io/ROS-Examples/
Port of the book in Python and Bambi: https://github.com/bambinos/Bambi_resources/tree/master/ROS
Andrew's home page: http://www.stat.columbia.edu/~gelman/
Andrew's blog: https://statmodeling.stat.columbia.edu/
Andrew on Twitter: https://twitter.com/statmodeling
Jennifer's home page:

#19 Turing, Julia and Bayes in Economics, with Cameron Pfiffer Jul 03, 2020

Show notes

Do you know Turing? Of course you do! With Soss and Gen, it’s one of the blockbusters to do probabilistic programming in Julia. And in this episode Cameron Pfiffer will tell us all about it — how it came to life, how it fits into the probabilistic programming landscape, and what its main strengths and weaknesses are.

Cameron did some Rust, some Python, but he especially loves coding in Julia. That’s also why he’s one of the core-developers of Turing.jl. He’s also a PhD student in finance at the University of Oregon and did his master’s in finance at the University of Reading. His interests are pretty broad, from cryptocurrencies, algorithmic and high-frequency trading, to AI in financial markets and anomaly detection – in a nutshell he’s a fan of topics where technology is involved.

As he’s the first economist to come to the show, I also asked him how Bayesian the field of economics is, why he thinks economics is quite unique among the social sciences, and how economists think about causality — I later learned that this topic is pretty controversial!

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

Links from the show:

Bayesian Econometrics on Cameron's Blog: http://cameron.pfiffer.org/2020/03/24/bayesian-econometrics/
Cameron on Twitter: https://twitter.com/cameron_pfiffer
Cameron on GitHub: https://github.com/cpfiffer
Turing.jl -- Bayesian inference in Julia: https://turing.ml/dev/
Gen.jl -- Programmable inference embedded in Julia: https://www.gen.dev/
Soss.jl -- Probabilistic programming via source rewriting: https://github.com/cscherrer/Soss.jl
The Julia Language -- A fresh approach to technical computing: https://julialang.org/
What is Probabilistic Programming -- Cornell University: http://adriansampson.net/doc/ppl.html
Mostly Harmless Econometrics Book: http://www.mostlyharmlesseconometrics.com/

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.

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