Aki Vehtari's guest appearances

Aki Vehtari

Learning Bayesian Statistics

David Spiegelhalter

Andrew Gelman

Michael Lee

Baba Brinkman

Michael Kennedy

Allen Downey

Sean Taylor

Michael Faber

Matthew Kay

Michael Betancourt

Eric Ma

Karin Knudson

Colin Carroll

Osvaldo Martin

Chris Fonnesbeck

OwlTail

I’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we?So, Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. You already heard his delightful Finnish accent on episode 20, with Andrew Gelman and Jennifer Hill, talking about their latest book, « Regression and other stories ». He is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and a core-developer of the seminal probabilistic programming framework Stan.An enthusiast of open-source software, Aki is a core-contributor to the ArviZ package and has been involved in many free software projects such as GPstuff for Gaussian processes and ELFI for likelihood inference.His numerous research interests are Bayesian probability theory and methodology, especially model assessment and selection, non-parametric models (such as Gaussian processes), feature selection, dynamic models, and hierarchical models.We talked about all that — and more — on this episode, in the context of his teaching at Aalto and the software-assisted Bayesian workflow he’s currently working on with a group of researchers.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, Paul Oreto, Colin Caprani, George Ho and Colin Carroll.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:New podcast website: https://www.learnbayesstats.com/Rate LBS on Podchaser: https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588Aki's website: https://users.aalto.fi/~ave/Aki's educational material: https://avehtari.github.io/Aki on GitHub: https://github.com/avehtariAki on Twitter: https://twitter.com/avehtariBayesian Data Analysis, 3rd edition: https://www.routledge.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955Bayesian Data Analysis course material: https://github.com/avehtari/BDA_course_AaltoRegression and Other Stories: https://avehtari.github.io/ROS-Examples/Aki’s favorite scientific books (so far): https://statmodeling.stat.columbia.edu/2018/05/14/aki_books/Aki's talk on Agile Probabilistic Programming: https://www.youtube.com/watch?v=cHlPgHn6btgAki's posts on Andrew Gelman's blog: https://statmodeling.stat.columbia.edu/author/aki/Stan software: https://mc-stan.org/GPstuff - Gaussian process models for Bayesian analysis: https://research.cs.aalto.fi/pml/software/gpstuff/Projpred -- R package to perform projection predictive variable selection for GLMs: https://github.com/stan-dev/projpredThis podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

I’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we?So, Aki is an Associate professor in computational probabilistic modeling at Aalto University, Finland. You already heard his delightful Finnish accent on episode 20, with Andrew Gelman and Jennifer Hill, talking about their latest book, « Regression and other stories ». He is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and a core-developer of the seminal probabilistic programming framework Stan.An enthusiast of open-source software, Aki is a core-contributor to the ArviZ package and has been involved in many free software projects such as GPstuff for Gaussian processes and ELFI for likelihood inference.His numerous research interests are Bayesian probability theory and methodology, especially model assessment and selection, non-parametric models (such as Gaussian processes), feature selection, dynamic models, and hierarchical models.We talked about all that — and more — on this episode, in the context of his teaching at Aalto and the software-assisted Bayesian workflow he’s currently working on with a group of researchers.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !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 and Colin Carroll.Visit <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer nofollow" target="_blank">https://www.patreon.com/learnbayesstats</a> to unlock exclusive Bayesian swag ;)Links from the show:<ul><li>New podcast website: <a href="https://www.learnbayesstats.com/" rel="noopener noreferrer nofollow" target="_blank">https://www.learnbayesstats.com/</a></li><li>Rate LBS on Podchaser: <a href="https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588" rel="noopener noreferrer nofollow" target="_blank">https://www.podchaser.com/podcasts/learning-bayesian-statistics-932588</a></li><li>Aki's website: <a href="https://users.aalto.fi/~ave/" rel="noopener noreferrer nofollow" target="_blank">https://users.aalto.fi/~ave/</a></li><li>Aki's educational material: <a href="https://avehtari.github.io/" rel="noopener noreferrer nofollow" target="_blank">https://avehtari.github.io/</a></li><li>Aki on GitHub: <a href="https://github.com/avehtari" rel="noopener noreferrer nofollow" target="_blank">https://github.com/avehtari</a></li><li>Aki on Twitter: <a href="https://twitter.com/avehtari" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/avehtari</a></li><li>Bayesian Data Analysis, 3rd edition: <a href="https://www.routledge.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955" rel="noopener noreferrer nofollow" target="_blank">https://www.routledge.com/Bayesian-Data-Analysis/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/p/book/9781439840955</a></li><li>Bayesian Data Analysis course material: <a href="https://github.com/avehtari/BDA_course_Aalto" rel="noopener noreferrer nofollow" target="_blank">https://github.com/avehtari/BDA_course_Aalto</a></li><li>Regression and Other Stories: <a href="https://avehtari.github.io/ROS-Examples/" rel="noopener noreferrer nofollow" target="_blank">https://avehtari.github.io/ROS-Examples/</a></li><li>Aki’s favorite scientific books (so far): <a href="https://statmodeling.stat.columbia.edu/2018/05/14/aki_books/" rel="noopener noreferrer nofollow" target="_blank">https://statmodeling.stat.columbia.edu/2018/05/14/aki_books/</a></li><li>Aki's talk on Agile Probabilistic Programming: <a href="https://www.youtube.com/watch?v=cHlPgHn6btg" rel="noopener noreferrer nofollow" target="_blank">https://www.youtube.com/watch?v=cHlPgHn6btg</a></li><li>Aki's posts on Andrew Gelman's blog: <a href="https://statmodeling.stat.columbia.edu/author/aki/" rel="noopener noreferrer nofollow" target="_blank">https://statmodeling.stat.columbia.edu/author/aki/</a></li><li>Stan software: <a href="https://mc-stan.org/" rel="noopener noreferrer nofollow" target="_blank">https://mc-stan.org/</a></li><li>GPstuff - Gaussian process models for Bayesian analysis: <a href="https://research.cs.aalto.fi/pml/software/gpstuff/" rel="noopener noreferrer nofollow" target="_blank">https://research.cs.aalto.fi/pml/software/gpstuff/</a></li><li>Projpred -- R package to perform projection predictive variable selection for GLMs: <a href="https://github.com/stan-dev/projpred" rel="noopener noreferrer nofollow" target="_blank">https://github.com/stan-dev/projpred</a></li></ul>This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

#29 Model Assessment, Non-Parametric Models, And Much More, with Aki Vehtari

Jennifer Hill

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/GoodBayesian2020Amazon 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/110702398XCode, 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/ROSAndrew's home page: http://www.stat.columbia.edu/~gelman/Andrew's blog: https://statmodeling.stat.columbia.edu/Andrew on Twitter: https://twitter.com/statmodelingJennifer's home page: https://steinhardt.nyu.edu/people/jennifer-hillAki's teaching material: https://avehtari.github.io/Aki's home page: https://users.aalto.fi/~ave/Aki on Twitter: https://twitter.com/avehtariThank 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.This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

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 <a href="http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020" rel="noopener noreferrer nofollow" target="_blank">http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020</a> 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 <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !Links from the show:<ul><li>Regression and Other Stories on Cambridge Press website: <a href="http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020" rel="noopener noreferrer nofollow" target="_blank">http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020</a></li><li>Amazon page (because of VAT laws, in some regions ordering from Amazon can be cheaper than from the editor directly, even with the discount): <a href="https://www.amazon.com/Regression-Stories-Analytical-Methods-Research/dp/110702398X" rel="nofollow" target="_blank">https://www.amazon.com/Regression-Stories-Analytical-Methods-Research/dp/110702398X</a></li><li>Code, data and examples for the book: <a href="https://avehtari.github.io/ROS-Examples/" rel="noopener noreferrer nofollow" target="_blank">https://avehtari.github.io/ROS-Examples/</a></li><li>Port of the book in Python and Bambi: <a href="https://github.com/bambinos/Bambi_resources/tree/master/ROS" rel="noopener noreferrer nofollow" target="_blank">https://github.com/bambinos/Bambi_resources/tree/master/ROS</a></li><li>Andrew's home page: <a href="http://www.stat.columbia.edu/~gelman/" rel="noopener noreferrer nofollow" target="_blank">http://www.stat.columbia.edu/~gelman/</a></li><li>Andrew's blog: <a href="https://statmodeling.stat.columbia.edu/" rel="noopener noreferrer nofollow" target="_blank">https://statmodeling.stat.columbia.edu/</a></li><li>Andrew on Twitter: <a href="https://twitter.com/statmodeling" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/statmodeling</a></li><li>Jennifer's home page: <a href="https://steinhardt.nyu.edu/people/jennifer-hill" rel="noopener noreferrer nofollow" target="_blank">https://steinhardt.nyu.edu/people/jennifer-hill</a></li><li>Aki's teaching material: <a href="https://avehtari.github.io/" rel="noopener noreferrer nofollow" target="_blank">https://avehtari.github.io/</a></li><li>Aki's home page: <a href="https://users.aalto.fi/~ave/" rel="noopener noreferrer nofollow" target="_blank">https://users.aalto.fi/~ave/</a></li><li>Aki on Twitter: <a href="https://twitter.com/avehtari" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/avehtari</a></li></ul>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.This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

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