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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:

#9 Exploring the Cosmos with Bayes and Maggie Lieu Feb 12, 2020

Show notes

Have you always wondered what dark matter is? Can we even see it — let alone measure it? And what would discover it imply for our understanding of the Universe?

In this episode, we’ll take look at the cosmos with Maggie Lieu. She’ll tell us what research in astrophysics is made of, what model she worked on at the European Space Agency, and how Bayesian the world of space science is.

Maggie Lieu did her PhD in the Astronomy & Space Department of the University of Birmingham. She’s now a Research Fellow of Machine Learning & Cosmology at the University of Nottingham and is working on projects in preparation for Euclid, a space-based telescope whose goal is to map the dark Universe and help us learn about the nature of dark matter and dark energy.

In a nutshell, she tries to help us better understand the entire cosmos. Even more amazing, she uses the Stan library and applies Bayesian statistical methods to decipher her astronomical data! But Maggie is not just a Bayesian astrophysicist: she also loves photography and rock-climbing!

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:

Maggie's Website: https://maggielieu.com/
Maggie's Google Scholar Page: https://scholar.google.co.uk/citations?user=ilfwfuUAAAAJ&hl=en
Maggie on Twitter: https://twitter.com/Space_Mog
Maggie on GitHub: https://github.com/MaggieLieu
Maggie on YouTube: https://www.youtube.com/channel/UClO6TuRE6XLzbMBmQ_KY38A
Stan -- Statistical Modeling Platform: https://mc-stan.org/
Stan's YouTube Channel: https://www.youtube.com/channel/UCwgN5srGpBH4M-Zc2cAluOA

#8 Bayesian Inference for Software Engineers, with Max Sklar Jan 29, 2020

Show notes

What is it like using Bayesian tools when you’re a software engineer or computer scientist? How do you apply these tools in the online ad industry?

More generally, what is Bayesian thinking, philosophically? And is it really useful in every day life? Because, well you can’t fire up MCMC each time you need to make a quick decision under uncertainty… So how do you do that in practice, when you have at most a pen and paper?

In this episode, you’ll hear Max Sklar’s take on these questions. Max is a software engineer with a focus on machine learning and Bayesian inference. Now working at Foursquare’s innovation lab, he recently led the development of a causality model for Foursquare’s Ad Attribution product and taught a course on Bayesian Thinking at the Lviv Data Science Summer School.

Max is also an open-source enthusiast and a fellow podcaster – he’s the host of the Local Maximum podcast, where you can hear every week about the latest trends in AI, machine learning and technology from an engineering perspective.

Ow, and if you liked the movie « Her », with Joaquin Phoenix, well you’re in for a treat at the end of this 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:

Local Maximum podcast website: https://www.localmaxradio.com
Max on Twitter: https://twitter.com/maxsklar
Bayesian linear models: https://github.com/maxsklar/BayesPy/tree/master/LinearModels
Bayesian Dirichlet-Multinomial estimation: https://github.com/maxsklar/BayesPy/tree/master/DirichletEstimation
Bayesian Thinking for Applied Machine Learning slides: https://docs.google.com/presentation/d/1eiceuvXlsoFKoHdqjF3qXBkyht7vR0YXQPG82ady-TU/edit?usp=sharing

#7 Designing a Probabilistic Programming Language & Debugging a Model, with Junpeng Lao Jan 16, 2020

Show notes

You can’t study psychology up until your PhD and end-up doing very mathematical and computational data science at Google right? It’s too hard of a U-turn — some would even say it’s NUTS, just because they like bad puns… Well think again, because Junpeng Lao did just that!

Before doing data science at Google, Junpeng was a cognitive psychology researcher at the University of Fribourg, Switzerland. Working in Python, Matlab and occasionally in R, Junpeng is a prolific open-source contributor, particularly to the popular TensorFlow and PyMC3 libraries. He also maintains the PyMC Discourse on his free time, where he amazingly answers all kinds of various and very specific questions!

In this episode, he’ll tell you what the core characteristics of TensorFlow Probability are, and when you would use TFP instead of another probabilistic programming framework, like Stan or PyMC3. He’ll also explain why PyMC4 will be based on TensorFlow Probability itself, and what future contributions he has in mind for these two amazing libraries. Finally, Junpeng will share with you his workflow for debugging a model, or just for better understanding your models.

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:

Junpeng's blog: https://junpenglao.xyz/
Junpeng on Twitter: https://twitter.com/junpenglao
Junpeng on GitHub: https://github.com/junpenglao
Advanced Bayesian Modeling Tutorial: https://discourse.pymc.io/t/advance-bayesian-modelling-with-pymc3/1439
Stan Devs' Prior Choice Recommendations: https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations
PyMC Discourse: https://discourse.pymc.io/
PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/
Tensor Flow Probability: https://www.tensorflow.org/probability/

#6 A principled Bayesian workflow, with Michael Betancourt Jan 03, 2020

Show notes

If you’re there, it’s probably because you’re interested in Bayesian inference, right? But don’t you feel lost sometimes when building a model? Or you ask yourself why what you’re trying to do is so damn hard… and you conclude that YOU are the problem, that YOU must be doing something wrong!

Well, rest assured, as you’ll hear from Michael Betancourt himself: it’s hard for everybody! That’s why over the years he developed and tries to popularize what he calls a « principled Bayesian workflow » — in a nutshell, think about what could have generated your data; and always question default settings!

With that workflow, you’ll probably feel less alone when modeling, but expect to fail often. That’s ok — as Michael says: if you don’t fail, you don’t learn!

Who is Michael Betancourt you ask? He is a physicist and statistician, whose research focuses on the development of robust statistical workflows, computational tools, and pedagogical resources that help bridge the gap between statistical theory and scientific practice.

Michael works a lot on differential geometry and probability theory, and he often lives in high-dimensional spaces, where he meets with a good friend of his -- Hamiltonian Monte Carlo. Then, you won’t be surprised to learn that Michael is one of the core developers of the seminal probabilistic programming language Stan.

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:

Michael's upcoming course: https://events.eventzilla.net/e/introduction-to-bayesian-inference-with-stan-with-michael-betancourt-2138756860
Michael's website (the “Writing” page collects the case studies and pedagogical material, and the “Speaking” page links to the recorded talks): https://betanalpha.github.io/
Support Michael's work on Patreon: https://patreon.com/betanalpha
Michael on Twitter: https://twitter.com/betanalpha
Michael on GitHub: https://github.com/betanalpha
Stan probabilistic programming langage: https://mc-stan.org/

#5 How to use Bayes in the biomedical industry, with Eric Ma Dec 17, 2019

Show notes

I have two questions for you: Are you a self-learner? Then how do you stay up to date? What should you focus on if you’re a beginner, or if you’re more advanced?

And here is my second question: Are you working in biomedicine? And if you do, are you using Bayesian tools? Then how do you get your co-workers more used to posterior distributions than p-values? In other words, how do you change behaviors in a large organization?

In this episode, Eric Ma will answer all these questions and even tell us his favorite modeling techniques, which problems he encountered with these models, and how he solved them. He’ll also share with us the software-engineering workflow he uses at Novartis to share his work with colleagues.

Eric is a data scientist at the Novartis Institutes for Biomedical Research, where he focuses on Bayesian statistical methods to make medicines for patients. Eric is also a prolific open source developer: he led the development of pyjanitor, an API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He also contributes to PyMC3, matplotlib and bokeh.

This is « Learning Bayesian Statistics », episode 5, recorded October 21, 2019.

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:

Eric's website: https://ericmjl.github.io/
Eric on Twitter: https://twitter.com/ericmjl
Bayesian analysis recipes: https://github.com/ericmjl/bayesian-analysis-recipes
Bayesian deep learning demystified: https://github.com/ericmjl/bayesian-deep-learning-demystified
Causality repo: https://github.com/ericmjl/causality
Pyjanitor - Convenient data cleaning routines for repetitive tasks: https://pyjanitor.readthedocs.io/
PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/
Panel - A high-level app and dashboarding solution for Python: https://panel.pyviz.org/
Nxviz - Visualization Package for NetworkX: https://nxviz.readthedocs.io/en/latest/

#4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson Dec 04, 2019

Show notes

What do neurodegenerative diseases, gerrymandering and ecological inference all have in common? Well, they can all be studied with Bayesian methods — and that’s exactly what Karin Knudson is doing.

In this episode, Karin will share with us the vital and essential work she does to understand aspects of neurodegenerative diseases. She’ll also tell us more about computational neuroscience and Dirichlet processes — what they are, what they do, and when you should use them.

Karin did her doctorate in mathematics, with a focus on compressive sensing and computational neuroscience at the University of Texas at Austin. Her doctoral work included applying hierarchical Dirichlet processes in the setting of neural data and focused on one-bit compressive sensing and spike-sorting.

Formerly the chair of the math and computer science department of Phillips Academy Andover, she started a postdoc at Mass General Hospital and Harvard Medical in Fall 2019. Most importantly, rock climbing and hiking have no secrets for her!

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, personally curated by Karin Knudson:

Karin on Twitter: https://twitter.com/karinknudson
Spike train entropy-rate estimation using hierarchical Dirichlet process priors (Knudson and Pillow): https://pillowlab.princeton.edu/pubs/abs_Knudson_HDPentropy_NIPS13.html
Fighting Gerrymandering with PyMC3, PyCon 2018, Colin Carroll and Karin Knudson: https://www.youtube.com/watch?v=G9I5ZnkWR0A
Expository resources on Dirichlet Processes: Chapter 23 of Bayesian Data Analysis (Gelman et al.) and http://www.gatsby.ucl.ac.uk/~ywteh/research/npbayes/dp.pdf
Hierarchical Dirichlet Processes (introduced the HDP and included applications in topic modeling and for working with time-series data and Hidden Markov Models): https://www.stat.berkeley.edu/~aldous/206-Exch/Papers/hierarchical_dirichlet.pdf
A Sticky HDP-HMM with applications to speaker diarization (a nice example of how the HDP can be used with HMM, in this case cleverly adapted so that states have more persistence): https://arxiv.org/abs/0905.2592
If you want to get deeper into the weeds and also get a sense of the history: Dirichlet Processes with Applications to Bayesian Nonparametric Problems (https://projecteuclid.org/euclid.aos/1176342871) and A Bayesian Analysis of Some Nonparametric Problems (https://projecteuclid.org/euclid.aos/1176342360)

#3.2 How to use Bayes in industry, with Colin Carroll Nov 18, 2019

Show notes

How can you use Bayesian tools and optimize your models in industry? What are the best ways to communicate and visualize your models with non-technical and executive people? And what are the most common pitfalls?

In this episode, Colin Carroll will tell us how he did all that in finance and the airline industry. He’ll also share with us what the future of probabilistic programming looks like to him.

You already heard from Colin two weeks ago — so, if you didn’t catch this episode, go back in your feed’s history and enjoy the first part!

As a reminder, Colin is a machine learning researcher and software engineer who’s notably worked on modeling risk in the airline industry and building NLP-powered search infrastructure for finance. He’s also an active contributor to open source, particularly to the popular PyMC3 and ArviZ libraries.

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:

Colin's blog: https://colindcarroll.com/
Gelman’s putting model in PyMC3: https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/putting_workflow.ipynb
Matthew Kay’s quantile dotplots: https://github.com/mjskay/when-ish-is-my-bus/blob/master/quantile-dotplots.md
Jax, Composable transformations of Python+NumPy programs: https://github.com/google/jax
NumPyro, Probabilistic programming with NumPy: https://github.com/pyro-ppl/numpyro
Pyro, Deep Universal Probabilistic Programming: https://pyro.ai/
Rainier, Bayesian inference in Scala: https://github.com/stripe/rainier

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Send in a voice message: https://anchor.fm/learn-bayes-stats/message

#3.1 What is Probabilistic Programming & Why use it, with Colin Carroll Nov 05, 2019

Show notes

When speaking about Bayesian statistics, we often hear about « probabilistic programming » — but what is it? Which languages and libraries allow you to program probabilistically? When is Stan, PyMC, Pyro or any other probabilistic programming language most appropriate for your project? And when should you even use Bayesian libraries instead of non-bayesian tools, like Statsmodels or Scikit-learn?

Colin Carroll will answer all these questions for you. Colin is a machine learning researcher and software engineer who’s notably worked on modeling risk in the airline industry and building NLP-powered search infrastructure for finance. He’s also an active contributor to open source, particularly to the popular PyMC3 and ArviZ libraries.

Having studied geometric measure theory at Rice University, Colin was bound to walk in the woods with Pete the pup – who was there when we recorded by the way – and to launch balloons into near-space in his spare time.

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:

Colin's blog: https://colindcarroll.com/
Colin on Twitter: https://twitter.com/colindcarroll
Colin on GitHub: https://github.com/ColCarroll
Very parallel MCMC sampling: https://colindcarroll.com/2019/08/18/very-parallel-mcmc-sampling/
A tour of probabilistic programming APIs: https://colindcarroll.com/2019/07/23/a-tour-of-probabilistic-programming-apis/
PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/
Stan: https://mc-stan.org/
Pyro, Deep Universal Probabilistic Programming: https://pyro.ai/
ArviZ, Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/
PyMC-Learn, Probabilistic models for machine learning: https://www.pymc-learn.org/
Facebook’s Prophet uses Stan: https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/
Prophet in PyMC3: https://github.com/luke14free/pm-prophet

#2 When should you use Bayesian tools, and Bayes in sports analytics, with Chris Fonnesbeck Oct 23, 2019

Show notes

When are Bayesian methods most useful? Conversely, when should you NOT use them? How do you teach them? What are the most important skills to pick-up when learning Bayes? And what are the most difficult topics, the ones you should maybe save for later?

In this episode, you’ll hear Chris Fonnesbeck answer these questions from the perspective of marine biology and sports analytics. Chris is indeed the New York Yankees’ senior quantitative analyst and an associate professor at Vanderbilt University School of Medicine.

He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He also created PyMC, a library to do probabilistic programming in python, and is the author of several tutorials at PyCon and PyData conferences.

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:

Chris on Twitter: https://twitter.com/fonnesbeck
PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/
Chris on GitHub: https://github.com/fonnesbeck
An introduction to Markov Chain Monte Carlo using PyMC3 - PyData London 2019: https://www.youtube.com/watch?v=SS_pqgFziAg
Introduction to Statistical Modeling with Python - PyCon 2017 - video: https://www.youtube.com/watch?v=TMmSESkhRtI
Introduction to Statistical Modeling with Python - PyCon 2017 - code repo: https://github.com/fonnesbeck/intro_stat_modeling_2017
Bayesian Non-parametric Models for Data Science using PyMC3 - PyCon 2018: https://www.youtube.com/watch?v=-sIOMs4MSuA
Statistical Data Analysis in Python: https://github.com/fonnesbeck/statistical-analysis-python-tutorial

#1 Bayes, open-source and bioinformatics, with Osvaldo Martin Oct 08, 2019

Show notes

What do you get when you put a physicist, a biologist and a data scientist in the same body? Well, you’re about to find out…

In this episode you’ll meet Osvaldo Martin. Osvaldo is a researcher at the National Scientific and Technical Research Council in Argentina and is notably the author of the book Bayesian Analysis with Python, whose second edition was published in December 2018.

He also teaches bioinformatics, data science and Bayesian data analysis, and is a core developer of PyMC3 and ArviZ, and recently started contributing to Bambi. Originally a biologist and physicist, Osvaldo trained himself to python and Bayesian methods – and what he’s doing with it is pretty amazing!

We also touch on how accepted are Bayesian methods in his field, which models he’s currently working on, and what it’s like to be an open-source developer.

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 Analysis with Python, 2nd edition: https://www.amazon.com/dp/B07HHBCR9G
Bayesian Analysis with Python, code repository; https://github.com/aloctavodia/BAP
Osvaldo on Twitter: https://twitter.com/aloctavodia
PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/
ArviZ, Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/
BAyesian Model-Building Interface (BAMBI) in Python: https://bambinos.github.io/bambi/

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