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    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!

    Advertise

    Copyright: © Copyright Alexandre Andorra

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    Latest Episodes:
    #SpecialAnnouncement: Patreon Launched! Jun 26, 2020
    Show notes

    I hope you’re all safe! Some of you also asked me if I had set up a Patreon so that they could help support the show, and that’s why I’m sending this short special episode your way today. I had thought about that, but I wasn’t sure there was a demand for this. Apparently, there is one — at least a small one — so, first, I wanna thank you and say how grateful I am to be in a community that values this kind of work!

    The Patreon page is now live at patreon.com/learnbayesstats. It starts as low as 3€ and you can pick from 4 different tiers:

    1. "Maximum A Posteriori" (3€): Join the Slack, where you can ask questions about the show, discuss with like-minded Bayesians and meet them in-person when you travel the world.
    2. "Full Posterior" (5€): Previous tier + Your name in all the show notes, and I'll express my gratitude to you in the first episode to go out after your contribution. You also get early access to the special episodes. -- that I'll make at an irregular pace and will include panel discussions, book releases, live shows, etc.
    3. "Principled Bayesian" (20€): Previous tiers + Every 2 months, I'll ask my guest two questions voted-on by "Principled Bayesians". I'll probably do that with a poll in the Slack channel, which will be only answered by the "Principled Bayesians" and of these questions, I will ask the top 2 every two months on the show.
    4. "Good Bayesian" (200€, only 8 spots): Previous tiers + Every 2 months, you can come on the show and you ask one question to the guest without a vote. So that's why I can't have too many people in that tier.

    Before telling you the best part: I already have a lot of ideas for exclusive content and options. I first need to see whether you're as excited as I am about it. If I see you are, I'll be able to add new perks to the tiers! So give me your feedback about the current tiers or any benefits you'd like to see there... but don't see yet! BTW, you have a new way to do that now: sending me voice messages at anchor.fm/learn-bayes-stats/message!

    Now, the icing on the cake: until July 31st, if you choose the "Full Posterior" tier (5$) or higher, you get early access to the very special episode I'm planning with Andrew Gelman, Jennifer Hill and Aki Vehtari about their upcoming book, "Regression and other stories". To top it off, there will be a promo code in the episode to buy the book at a discount price — now, that is an offer you can't turn down!

    Alright, that is it for today — I hope you’re as excited as I am for this new stage in the podcast’s life! Please keep the emails, the tweets, the voice messages, the carrier pigeons coming with your feedback, questions and suggestions.

    In the meantime, take care and I’ll see you in the next episode — episode 19, with Cameron Pfiffer, who’s the first economist to come on the show and who’s a core-developer of Turing.jl. We’re gonna talk about the Julia probabilistic programming landscape, Bayes in economics and causality — it’s gonna be fun ;)

    Again, patreon.com/learnbayesstats if you want to support the show and unlock some nice perks. Thanks again, I am very grateful for any support you can bring me!

    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:

    • LBS Patreon page: patreon.com/learnbayesstats
    • Send me voice messages:

    #18 How to ask good Research Questions and encourage Open Science, with Daniel Lakens Jun 18, 2020
    Show notes

    How do you design a good experimental study? How do you even know that you’re asking a good research question? Moreover, how can you align funding and publishing incentives with the principles of an open source science?

    Let’s do another “big picture” episode to try and answer these questions! You know, these episodes that I want to do from time to time, with people who are not from the Bayesian world, to see what good practices there are out there. The first one, episode 15, was focused on programming and python, thanks to Michael Kennedy.

    In this one, you’ll meet Daniel Lakens. Daniel is an experimental psychologist at the Human-Technology Interaction group at Eindhoven University of Technology, in the Netherlands. He’s worked there since 2010, when he received his PhD in social psychology.

    His research focuses on how to design and interpret studies, applied meta-statistics, and reward structures in science. Daniel loves teaching about research methods and about how to ask good research questions. He even crafted free Coursera courses about these topics.

    A fervent advocate of open science, he prioritizes scholar articles review requests based on how much the articles adhere to Open Science principles. On his blog, he describes himself as ‘the 20% Statistician’. Why? Well, he’ll tell you 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:

    • Daniel's website: https://sites.google.com/site/lakens2/Home
    • The 20% Statistician: http://daniellakens.blogspot.com/
    • Daniel on GitHub: https://github.com/Lakens
    • Daniel on Twitter: https://twitter.com/lakens
    • Daniel on Google Scholar: https://scholar.google.nl/citations?user=ZbqYyrsAAAAJ&hl=nl
    • Coursera Course -- Improving your statistical inferences: https://www.coursera.org/learn/statistical-inferences
    • Coursera Course -- Improving Your Statistical Questions: https://www.coursera.org/learn/improving-statistical-questions
    • Peer Reviewers' Openness Initiative: https://opennessinitiative.org/
    • The Scientific Paper Is Obsolete -- Here’s what’s next: https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/


    #17 Reparametrize Your Models Automatically, with Maria Gorinova Jun 04, 2020
    Show notes

    Have you already encountered a model that you know is scientifically sound, but that MCMC just wouldn’t run? The model would take forever to run — if it ever ran — and you would be greeted with a lot of divergences in the end. Yeah, I know, my stress levels start raising too whenever I hear the word « divergences »…

    Well, you’ll be glad to hear there are tricks to make these models run, and one of these tricks is called re-parametrization — I bet you already heard about the poorly-named non-centered parametrization?

    Well fear no more! In this episode, Maria Gorinova will tell you all about these model re-parametrizations! Maria is a PhD student in Data Science & AI at the University of Edinburgh. Her broad interests range from programming languages and verification, to machine learning and human-computer interaction.

    More specifically, Maria is interested in probabilistic programming languages, and in exploring ways of applying program-analysis techniques to existing PPLs in order to improve usability of the language or efficiency of inference.

    As you’ll hear in the episode, she thinks a lot about the language aspect of probabilistic programming, and works on the automation of various “tricks” in probabilistic programming: automatic re-parametrization, automatic marginalization, automatic and efficient model-specific inference.

    As Maria also has experience with several PPLs like Stan, Edward2 and TensorFlow Probability, she’ll tell us what she thinks a good PPL design requires, and what the future of PPLs looks like to 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:

    • Maria on the Web: http://homepages.inf.ed.ac.uk/s1207807/index.html
    • Maria on Twitter: https://twitter.com/migorinova
    • Maria on GitHub: https://github.com/mgorinova
    • Automatic Reparameterisation of Probabilistic Programs (Maria's paper with Dave Moore and Matthew Hoffman): https://arxiv.org/abs/1906.03028
    • Stan User's Guide on Reparameterization: https://mc-stan.org/docs/2_23/stan-users-guide/reparameterization-section.html
    • HMC for hierarchical models -- Background on reparameterization: https://arxiv.org/abs/1312.0906
    • NeuTra -- Automatic reparameterization: https://arxiv.org/abs/1903.03704
    • Edward2 -- A library for probabilistic modeling, inference, and criticism: http://edwardlib.org/
    • Pyro -- Automatic reparameterization and marginalization: https://pyro.ai/
    • Gen -- Programmable inference: http://probcomp.csail.mit.edu/software/gen/
    • TensorFlow Probability: https://www.tensorflow.org/probability/


    #16 Bayesian Statistics the Fun Way, with Will Kurt May 21, 2020
    Show notes

    A librarian, a philosopher and a statistician walk into a bar — and they can’t find anybody to talk to; nobody seems to understand what they are talking about. Nobody? No! There is someone, and this someone is Will Kurt!

    Will Kurt is the author of ‘Bayesian Statistics the Fun Way’ and ‘Get Programming With Haskell’. Currently the lead Data Scientist for the pricing and recommendations team at Hopper, he also blogs about stats and probability at countbayesie.com.

    In this episode, he’ll tell us how a Boston librarian can become a Data Scientist and work with Bayesian models everyday. He’ll also explain the value of Bayesian inference from a philosophical standpoint, why it’s useful in the travel industry and how his latest book came into life.

    Finally, Will is also a big fan of the “mind projection fallacy”, an informal fallacy first described by physicist and Bayesian philosopher Edwin Thompson Jaynes. Does that intrigue you? Well, stay tuned, he’ll tell us more 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:

    • Will's Blog: https://www.countbayesie.com
    • Will on Twitter: https://twitter.com/willkurt
    • Bayesian Statistics the Fun Way -- Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks: https://nostarch.com/learnbayes
    • Get Programming with Haskell: https://www.amazon.com/Get-Programming-Haskell-Will-Kurt/dp/1617293768
    • The Mind Projection Fallacy: https://en.wikipedia.org/wiki/Mind_projection_fallacy
    • Probability Theory -- The Logic of Science by E.T. Jaynes: https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99
    • Wittgenstein's Lectures on the Foundations of Mathematics: https://www.amazon.com/Wittgensteins-Lectures-Foundations-Mathematics-Cambridge/dp/0226904261


    #15 The role of Python in Science and Education, with Michael Kennedy May 06, 2020
    Show notes

    This is it folks! This is the first of the special episodes I want to do from time to time, to expand our perspective and get inspired by what’s going on elsewhere. The guests will not come directly from the Bayesian world, but will still be related to science or programming.

    For the first episode of the kind, I had the chance to chat with Michael Kennedy! Michael is not only a very knowledgeable and respected member of the Python community, he’s also the founder and host of Talk Python To Me, the most popular Python podcast. He’s the founder and chief author at Talk Python Training, where he develops many Python developer online courses.

    And before that, Michael was a professional software trainer for over 10 years – he has taught numerous developers throughout the world! But Michael is not only an entrepreneur and teacher – he’s also a father, a husband, and a proud inhabitant of Portland, OR!

    As you’ll hear, our conversation spanned a large array of topics — the role of Python in science and research; how it came to be so important in data science, and why; what are Python’s threats and weaknesses and how it should evolve to not become obsolete. Michael also has interesting thoughts on the role of programming in education and how it relates to geometry — but I’ll let you discover that one by yourself…

    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 on Twitter: https://twitter.com/mkennedy
    • The Talk Python Podcast: https://talkpython.fm/
    • The Python Bytes Podcast: https://pythonbytes.fm/
    • Michael's blog: https://blog.michaelckennedy.net/
    • Michael on Crowdcast: https://www.crowdcast.io/mkennedy
    • Jupytext -- Turn Jupyter Notebooks to scripts and (R) Markdown files: https://jupytext.readthedocs.io/en/latest/introduction.html


    #14 Hidden Markov Models & Statistical Ecology, with Vianey Leos-Barajas Apr 22, 2020
    Show notes

    I bet you love penguins, right? The same goes for koalas, or puppies! But what about sharks? Well, my next guest loves sharks — she loves them so much that she works a lot with marine biologists, even though she’s a statistician!

    Vianey Leos Barajas is indeed a statistician primarily working in the areas of statistical ecology, time series modeling, Bayesian inference and spatial modeling of environmental data. Vianey did her PhD in statistics at Iowa State University and is now a postdoctoral researcher at North Carolina State University.

    In this episode, she’ll tell us what she’s working on that involves sharks, sheep and other animals! Trying to model animal movements, Vianey often encounters the dreaded multimodal posteriors. She’ll explain why these can be very tricky to estimate, and why ecological data are particularly suited for hidden Markov models and spatio-temporal models — don’t worry, Vianey will explain what these models are 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:

    • Vianey on Twitter: https://twitter.com/vianey_lb
    • Hidden Markov Models in the Stan User's Guide: https://mc-stan.org/docs/2_18/stan-users-guide/hmms-section.html
    • Tagging Basketball Events with HMM in Stan: https://mc-stan.org/users/documentation/case-studies/bball-hmm.html
    • HMMs with Python and PyMC3: https://ericmjl.github.io/bayesian-analysis-recipes/notebooks/markov-models/
    • The Discrete Adjoint Method -- Efficient Derivatives for Functions of Discrete Sequences (Betancourt, Margossian, Leos-Barajas): https://arxiv.org/abs/2002.00326
    • Vianey will be doing an HMM 90-minute introduction at the International Statistical Ecology Conference in June 2020: http://www.isec2020.org/
    • Stan for Ecology -- a website for the ecology community in Stan: https://stanecology.github.io/
    • LatinR 2020 -- 7th to 9th October 2020: https://latin-r.com/
    • Migramar -- Science for the Conservation of Marine Migratory Species in the Eastern Pacific: http://migramar.org/hi/en/
    • Pelagios Kakunja -- Know, educate and conserve for a sustainable sea: https://www.pelagioskakunja.org/

    Book recommendations:

    • Hidden Markov Models for Time Series: https://www.routledge.com/Hidden-Markov-Models-for-Time-Series-An-Introduction-Using-R-Second-Edition/Zucchini-MacDonald-Langrock/p/book/9781482253832
    • Handbook of Mixture Analysis:

    #13 Building a Probabilistic Programming Framework in Julia, with Chad Scherrer Apr 08, 2020
    Show notes

    How is Julia doing? I’m talking about the programming language, of course! What does the probabilistic programming landscape in Julia look like? What are Julia’s distinctive features, and when would it be interesting to use it?

    To talk about that, I invited Chad Scherrer. Chad is a Senior Research Scientist at RelationalAI, a company that uses Artificial Intelligence technologies to solve business problems.

    Coming from a mathematics background, Chad did his PhD at Indiana University of Bloomington and has been working in statistics and data science for a decade now. Through this experience, he’s been using and developing probabilistic programming languages – so he’s familiar with python, R, PyMC, Stan and all the blockbusters of the field.

    But since 2018, he’s particularly interested in Julia and developed Soss, an open-source lightweight probabilistic programming package for Julia. In this episode, he’ll tell us why he decided to create this package, and which choices he made that made Soss what it is today. But we’ll also talk about other projects in Julia, like Turing or Gen for instance.

    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:

    • Chad's Website: https://cscherrer.github.io/
    • Chad on Twitter: https://twitter.com/ChadScherrer
    • Soss Package: https://github.com/cscherrer/Soss.jl
    • Soss Presentation at 2019 Strata NYC: https://slides.com/cscherrer/2019-09-26-strata#/
    • Passage -- A Parallel Sampler Generator for Hierarchical Bayesian Modeling: https://bit.ly/2UTmaYB
    • Dynamic HMC in Julia: https://github.com/tpapp/DynamicHMC.jl
    • Advanced HMC in Julia: https://github.com/TuringLang/AdvancedHMC.jl
    • Monte Carlo Measurements in Julia: https://github.com/baggepinnen/MonteCarloMeasurements.jl
    • Turing.jl -- Bayesian inference with probabilistic programming: https://turing.ml/dev/
    • Gen.jl -- Probabilistic modeling and inference in Julia: https://www.gen.dev/
    • Etalumis -- Bringing Probabilistic Programming to Scientific Simulators at Scale: https://arxiv.org/abs/1907.03382
    • Omega.jl -- A programming language for causal and probabilistic reasoning: http://www.zenna.org/Omega.jl/latest/
    • JuliaLang -- The Ingredients for a Composable Programming Language: https://white.ucc.asn.au/2020/02/09/whycompositionaljulia.html
    • Simpy -- Discrete event simulation for Python:

    #12 Biostatistics and Differential Equations, with Demetri Pananos Mar 25, 2020
    Show notes

    Do you know Google Summer of Code? It’s a time of year when students can contribute to open-source software by developing and adding much needed functionalities to the open-source package of their choice. And Demetri Pananos did just that.

    He did it in 2019 with PyMC3, for which he developed the API for ordinary differential equations. In this episode, he’ll tell us why and how he did that, what he learned from the experience, and what the strengths and weaknesses of the API are in his opinion.

    Demetri is a Ph.D candidate in Biostatistics at Western University, in Ontario, Canada. His research interests surround machine learning and Bayesian statistics for personalized medicine. He earned his Master’s in Applied Mathematics from The University of Waterloo and is a firm believer in open science, interdisciplinary collaboration, and reproducible research.

    Other than that, he loves plotting data and drinking IPA beer – well, who doesn’t?”

    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:

    • Demetri on Twitter: https://twitter.com/PhDemetri
    • Demetri on GitHub: https://github.com/Dpananos
    • Demetri's website: https://dpananos.github.io/
    • PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/
    • Chris Bishop, Pattern Recognition and Machine Learning: https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/0387310738
    • Bayesian Data Analysis (Gelman, Carlin, Stern, Dunson, Vehtari, Rubin): http://www.stat.columbia.edu/~gelman/book/
    • Parallel Plots: https://arviz-devs.github.io/arviz/generated/arviz.plot_parallel.html


    #11 Taking care of your Hierarchical Models, with Thomas Wiecki Mar 11, 2020
    Show notes

    I bet you already heard about hierarchical models, or multilevel models, or varying-effects models — yeah this type of models has a lot of names! Many people even turn to Bayesian tools to build _exactly_ these models. But what are they? How do you build and use a hierarchical model? What are the tricks and classical traps? And even more important: how do you _interpret_ a hierarchical model?

    In this episode, Thomas Wiecki will come to the rescue and explain what multilevel models are, how to build them, what their powers are… but also why you should be very careful when building them…

    Does the name Thomas Wiecki ring a bell? Probably because he’s the host and creator of the PyData Deep Dive Podcast, where he interviews open-source contributors from the Python and Data Science worlds! Thomas is also the VP of Data Science at Quantopian, a crowd-sourced quantitative investment firm that encourages people everywhere to write investment algorithms.

    Finally, Thomas is a longtime Bayesian and core-developer of PyMC3, a fantastic python package to do probabilistic programming in Python. On his blog, he publishes tutorial articles and explores new ideas such as Bayesian Deep Learning. Caring a lot about open-source software sustainability, he puts all he’s up to on his Patreon page, that you’ll find 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:

    • Thomas’ series on Hierarchical Regression: https://twiecki.io/blog/2013/08/12/bayesian-glms-1/
    • Non-centered Parametrization with PyMC3: https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/
    • Using Bayesian Decision Making: https://twiecki.io/blog/2019/01/14/supply_chain/
    • PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/
    • Symbolic PyMC: https://pymc-devs.github.io/symbolic-pymc/
    • PyData Deep Dive Podcast: https://pydata-podcast.com
    • Thomas on Twitter: https://twitter.com/twiecki?lang=en
    • Thomas on Patreon: https://www.patreon.com/twiecki
    • Thomas on GitHub: https://github.com/twiecki
    • Alex’s Hierarchical Model of Elections in Paris: https://mybinder.org/v2/gh/AlexAndorra/pollsposition_models/master?urlpath=%2Fvoila%2Frender%2Fdistrict-level%2Fmunic_model_analysis.ipynb


    #10 Exploratory Analysis of Bayesian Models, with ArviZ and Ari Hartikainen Feb 26, 2020
    Show notes

    How do you handle your MCMC samples once your Bayesian model fit properly? Which diagnostics do you check to see if there was a computational problem? And isn’t that nice when you have beautiful and reliable plots to complement your analysis and better understand your model?

    I know what you think: plotting can be long and complicated in these cases. Well, not with ArviZ, a platform-agnostic package to do exploratory analysis of your Bayesian models. And in this episode, Ari Hartikainen will tell you why.

    Ari is a data-scientist in geophysics and a researcher at the Department of Civil Engineering of Aalto University in Finland. He mainly works on geophysics, Bayesian statistics and visualization.

    Ari’s also a prolific open-source contributor, as he’s a core-developer of the popular Stan and ArviZ libraries. He’ll tell us how PyStan interacts with ArviZ, what he thinks ArviZ most useful features are, and which common difficulties he encounters with his models and data.

    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:

    • Ari on GitHub: https://github.com/ahartikainen
    • Ari on Twitter: https://twitter.com/a_hartikainen
    • ArviZ -- Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/
    • Introductory paper of ArviZ in The Journal of Open Source Software: https://www.researchgate.net/publication/330402908_ArviZ_a_unified_library_for_exploratory_analysis_of_Bayesian_models_in_Python
    • Stan -- Statistical Modeling Platform: https://mc-stan.org/
    • GPflow -- Gaussian processes in TensorFlow: https://www.gpflow.org/
    • GPy -- Gaussian processes framework in Python: https://sheffieldml.github.io/GPy/


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