41 episodes

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, and I live in Paris. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. 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 election forecasting and, most importantly, 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!

This podcast uses the following third-party services for analysis:

Podcorn - https://podcorn.com/privacy

Learning Bayesian Statistics Alexandre ANDORRA

    • Technology

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, and I live in Paris. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. 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 election forecasting and, most importantly, 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!

This podcast uses the following third-party services for analysis:

Podcorn - https://podcorn.com/privacy

    #38 How to Become a Good Bayesian (& Rap Artist), with Baba Brinkman

    #38 How to Become a Good Bayesian (& Rap Artist), with Baba Brinkman

    Episode sponsored by Tidelift: https://tidelift.com/ (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 great pleasure of hosting rap artist, science communicator and revered author of « Good Bayesian », Baba Brinkman!
    We talked about his passion for oral poetry, his rap career, what being a good rapper means and the difficulties he encounters to establish himself as a proper rapper.
    Baba began his rap career in 1998, freestyling and writing songs in his hometown of Vancouver, Canada.
    In 2000 he started adapting Chaucer’s Canterbury Tales into original rap compositions, and in 2004 he premiered a one man show based on his Master’s thesis, The Rap Canterbury Tales, exploring parallels between hip-hop music and medieval poetry.
    Over the years, Baba went on to create “Rap Guides” dedicated to scientific topics, like evolution, consciousness, medicine, religion, and climate change – and I encourage you to give them all a listen!
    By the way, do you know the common point between rap and evolutionary biology? Well, you’ll have to tune in for the answer… And make sure you listen until the end: Baba has a very, very nice surprise for you!
    A little tip: if you wanna enjoy it to the fullest, I put the unedited video version of this interview in the show notes ;) By the way, let me know if you like these video live streams — I might just do them again if you do!
    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (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, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski and Tim Radtke.
    Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
    Links from the show:
    Video live-stream of the episode: https://www.youtube.com/watch?v=YkFXpP_SvHk (https://www.youtube.com/watch?v=YkFXpP_SvHk)
    Baba on Twitter: https://twitter.com/bababrinkman (https://twitter.com/bababrinkman)
    Baba on YouTube: https://www.youtube.com/channel/UCz9Qm66ewnY0LAlZlL4HK9g (https://www.youtube.com/channel/UCz9Qm66ewnY0LAlZlL4HK9g)
    Baba on Spotify: https://open.spotify.com/artist/7DqKchcLvOIgR87RzJm3XH (https://open.spotify.com/artist/7DqKchcLvOIgR87RzJm3XH)
    Baba's website: https://bababrinkman.com/ (https://bababrinkman.com/)
    Event Rap Kickstarter: https://www.kickstarter.com/projects/bababrinkman/event-rap-the-one-stop-custom-rap-shop (https://www.kickstarter.com/projects/bababrinkman/event-rap-the-one-stop-custom-rap-shop)
    Event Rap website: https://www.eventrap.com/ (https://www.eventrap.com/)
    Anil Seth -- Your Brain Hallucinates your Conscious Reality: https://www.ted.com/talks/anil_seth_your_brain_hallucinates_your_conscious_reality (https://www.ted.c

    • 1 hr 27 min
    #37 Prophet, Time Series & Causal Inference, with Sean Taylor

    #37 Prophet, Time Series & Causal Inference, with Sean Taylor

    Episode sponsored by Tidelift: https://tidelift.com/ (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 about all that, who better than a time master, namely Sean Taylor?
    Sean is a co-creator of the Prophet time series package, available in R and Python. He’s a social scientist and statistician specialized in methods for solving causal inference and business decision problems. Sean is particularly interested in building tools for practitioners working on real-world problems, and likes to hang out with people from many fields — computer scientists, economists, political scientists, statisticians, machine learning researchers, business school scholars — although I guess he does that remotely these days…
    Currently head of the Rideshare Labs team at Lyft, Sean was a research scientist and manager on Facebook’s Core Data Science Team and did a PhD in information systems at NYU’s Stern School of Business. He did his undergraduate at the University of Pennsylvania, studying economics, finance, and information systems. Last but not least, he grew up in Philadelphia, so, of course, he’s a huge Eagles fan! For my non US listeners, we’re talking about the football team here, not the bird!
    We also talked about two of my favorite topics — science communication and epistemology — so I had a lot of fun talking with Sean, and I hope you’ll deem this episode a good investment of your 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/ (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, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen and Raul Maldonado.
    Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
    Links from the show:
    Sean's website: https://seanjtaylor.com/ (https://seanjtaylor.com/)
    Sean on GitHub: https://github.com/seanjtaylor (https://github.com/seanjtaylor)
    Sean on Twitter: https://twitter.com/seanjtaylor (https://twitter.com/seanjtaylor)
    Prophet docs: https://facebook.github.io/prophet/ (https://facebook.github.io/prophet/)
    Forecasting at Scale -- How and why we developed Prophet for forecasting at Facebook: https://www.youtube.com/watch?v=OaTAe4W9IfA (https://www.youtube.com/watch?v=OaTAe4W9IfA)
    Forecasting at Scale paper: https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1380080?journalCode=utas20and (https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1380080?journalCode=utas20and)
    TimeSeers -- Hierarchical version of Prophet, written in PyMC3: https://github.com/MBrouns/timeseers (https://github.com/MBrouns/timeseers)
    The Art of Doing Science and Engineering -- Learning to Learn: https://www.amazon.com/Art-Doing-Science-Engineering-Learning/dp/173226

    • 1 hr 6 min
    #36 Bayesian Non-Parametrics & Developing Turing.jl, with Martin Trapp

    #36 Bayesian Non-Parametrics & Developing Turing.jl, with Martin Trapp

    Episode sponsored by Tidelift: https://tidelift.com/ (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 learn about probabilistic circuits, sum-product networks and — what a delight — you’ll hear from the Julia community! Indeed, my guest for this episode is no other than… Martin Trapp!
    Martin is a core developer of Turing.jl, an open-source framework for probabilistic programming in Julia, and a post-doc in probabilistic machine learning at Aalto University, Finland.
    Martin loves working on sum-product networks and Bayesian non-parametrics. And indeed, his research interests focus on probabilistic models that exploit structural properties to allow efficient and exact computations while maintaining the capability to model complex relationships in data. In other words, Martin’s research is focused on tractable probabilistic models.
    Martin did his MsC in computational intelligence at the Vienna University of Technology and just finished his PhD in machine learning at the Graz University of Technology. He doesn’t only like to study the tractability of probabilistic models — he also is very found of 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/ (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, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen and Raul Maldonado.
    Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
    Links from the show:
    Martin's website: https://trappmartin.github.io/ (https://trappmartin.github.io/)
    Martin on GitHub: https://github.com/trappmartin (https://github.com/trappmartin)
    Martin on Twitter: https://twitter.com/martin_trapp (https://twitter.com/martin_trapp)
    Turing, Bayesian inference with Julia: https://turing.ml/dev/ (https://turing.ml/dev/)
    Hierarchical Dirichlet Processes: https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf (https://people.eecs.berkeley.edu/~jordan/papers/hdp.pdf)
    The Automatic Statistician: https://www.doc.ic.ac.uk/~mpd37/teaching/2014/ml_tutorials/2014-01-29-slides_zoubin2.pdf (https://www.doc.ic.ac.uk/~mpd37/teaching/2014/ml_tutorials/2014-01-29-slides_zoubin2.pdf)
    Truncated Random Measures: https://arxiv.org/abs/1603.00861 (https://arxiv.org/abs/1603.00861)
    Deep Structured Mixtures of Gaussian Processes: https://arxiv.org/abs/1910.04536 (https://arxiv.org/abs/1910.04536)
    Probabilistic Circuits -- Representations, Inference, Learning and Theory: https://www.youtube.com/watch?v=2RAG5-L9R70 (https://www.youtube.com/watch?v=2RAG5-L9R70)
    Applied Stochastic Differential Equations, from Simo Särkkä and Arno Solin: https://users.aalto.fi/~asolin/sde-book/sde-book.pdf (https://users.aalto.fi/~asolin/sde-book/sde-book.pdf)


    This pod

    • 1 hr 9 min
    #35 The Past, Present & Future of BRMS, with Paul Bürkner

    #35 The Past, Present & Future of BRMS, with Paul Bürkner

    Episode sponsored by Tidelift: https://tidelift.com/ (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 he created BRMS, in which fields it’s mostly used, what its weaknesses are, and which improvements to the package he’s currently working on. But that’s not it! Paul also gave his advice to people realizing that Bayesian methods would be useful to their research, but who fear facing challenges from advisors or reviewers.
    Besides being a Bayesian rockstar, Paul is a statistician working as an Independent Junior Research Group Leader at the Cluster of Excellence SimTech at the University of Stuttgart, Germany. Previously, he has studied Psychology and Mathematics at the Universities of Münster and Hagen and did his PhD in Münster about optimal design and Bayesian data analysis, and he also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University, Finland.
    So, of course, I asked him about the software-assisted Bayesian workflow that he’s currently working on with Aki Vehtari, which led us to no less than the future of probabilistic programming languages…
    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (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, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen and Jonathan Sedar.
    Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
    Links from the show:
    Paul's website: https://paul-buerkner.github.io/about/ (https://paul-buerkner.github.io/about/)
    Paul on Twitter: https://twitter.com/paulbuerkner (https://twitter.com/paulbuerkner)
    Paul on GitHub: https://github.com/paul-buerkner (https://github.com/paul-buerkner)
    BRMS docs: https://paul-buerkner.github.io/brms/ (https://paul-buerkner.github.io/brms/)
    Stan docs: https://mc-stan.org/ (https://mc-stan.org/)
    Bayesian workflow paper: https://arxiv.org/pdf/2011.01808v1.pdf (https://arxiv.org/pdf/2011.01808v1.pdf)


    This podcast uses the following third-party services for analysis:

    Podcorn - https://podcorn.com/privacy

    • 1 hr 7 min
    #34 Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy

    #34 Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy

    Episode sponsored by Tidelift: https://tidelift.com/ (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 statistical methods to analyze social science data. Working mainly with R and Stan, Lauren studies non-representative data, multilevel modeling, post-stratification, causal inference, and, more generally, how to make inferences from the social sciences.
    Needless to say that I asked her everything I could about MRP, including how to choose priors, why her recent paper about structured priors can improve MRP, and when MRP is not useful. We also talked about missing data imputation, and how all these methods relate to causal inference in the social sciences.
    If you want a bit of background, Lauren did her Undergraduates in Psychological Sciences and Maths and Computer Sciences at Adelaide University, with Danielle Navarro and Andrew Perfors, and then did her PhD with the same advisors. She spent 3 years in NYC with Andrew Gelman’s Lab at Columbia University, and then moved back to Melbourne in 2020. Most importantly, Lauren is an adept of crochet — she’s already on her third blanket!
    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (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, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege and Rémi Louf.
    Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
    Links from the show:
    Lauren's website: https://jazzystats.com/ (https://jazzystats.com/)
    Lauren on Twitter: https://twitter.com/jazzystats (https://twitter.com/jazzystats)
    Lauren on GitHub: https://github.com/lauken13 (https://github.com/lauken13)
    Improving multilevel regression and poststratification with structured priors: https://arxiv.org/abs/1908.06716 (https://arxiv.org/abs/1908.06716)
    Using model-based regression and poststratification to generalize findings beyond the observed sample: https://arxiv.org/abs/1906.11323 (https://arxiv.org/abs/1906.11323)
    Lauren's beginners Bayes workshop: https://github.com/lauken13/Beginners_Bayes_Workshop (https://github.com/lauken13/Beginners_Bayes_Workshop)
    MRP in RStanarm: https://github.com/lauken13/rstanarm/blob/master/vignettes/mrp.Rmd (https://github.com/lauken13/rstanarm/blob/master/vignettes/mrp.Rmd)
    Choosing your rstanarm prior with prior predictive checks: https://github.com/stan-dev/rstanarm/blob/vignette-prior-predictive/vignettes/prior-pred.Rmd (https://github.com/stan-dev/rstanarm/blob/vignette-prior-predictive/vignettes/prior-pred.Rmd)
    Mister P -- What’s its secret sauce?: https://statmodeling.stat.columbia.edu/2013/10/09/mister-p-whats-its-secret-sauce/ (https://statmodeling.stat.columbia.edu/2013/10/09/mister-p-whats-its-secret-sauce/)
    Bayesian Multilevel Estimation with Poststratification -- State-Level Estimates from Natio

    • 1 hr 12 min
    #33 Bayesian Structural Time Series, with Ben Zweig

    #33 Bayesian Structural Time Series, with Ben Zweig

    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 us about the Bayesian structural time series model they built to estimate inflows and outflows from companies, using LinkedIn data — a very challenging but fascinating endeavor, as you’ll hear!
    As a lot of people, Ben has always used more traditional statistical models but had been intrigued by Bayesian methods for a long time. When they started working on this Bayesian time series model though, he had to learn a bunch of new methods really quickly. I think you’ll find interesting to hear how it went…
    Ben also teaches data science and econometrics at the NYU Stern school of business, so he’ll reflect on his experience teaching Bayesian methods to economics students. Prior to that, Ben did a PhD in economics at the City University of New York, and has done research in occupational transformation and social mobility.
    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (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, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege and Rémi Louf.
    Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
    Links from the show:
    Ben's bio: https://www.stern.nyu.edu/faculty/bio/benjamin-zweig (https://www.stern.nyu.edu/faculty/bio/benjamin-zweig)
    Revelio Labs blog: https://www.reveliolabs.com/blog/ (https://www.reveliolabs.com/blog/)
    Predicting the Present with Bayesian Structural Time Series: https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdf (https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdf)
    A Hierarchical Framework for CorrectingUnder-Reporting in Count Data: https://arxiv.org/pdf/1809.00544.pdf (https://arxiv.org/pdf/1809.00544.pdf)
    TensorFlow Probability module for Bayesian structural time series models: https://www.tensorflow.org/probability/api_docs/python/tfp/sts/ (https://www.tensorflow.org/probability/api_docs/python/tfp/sts/)
    Fitting Bayesian structural time series with the bsts R package: https://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html (https://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html)
    CausalImpact, an R package for causal inference using Bayesian structural time-series models: https://cran.r-project.org/web/packages/CausalImpact/vignettes/CausalImpact.html (https://cran.r-project.org/web/packages/CausalImpact/vignettes/CausalImpact.html)


    This podcast uses the following third-party services for analysis:

    Podcorn - https://podcorn.com/privacy

    • 57 min

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