Junpeng Lao's guest appearances

Junpeng Lao

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

Ravin Kumar

You know when you have friends who wrote a book and pressure you to come on your podcast? That’s super annoying, right?Well that’s not what happened with Ravin Kumar, Osvaldo Martin and Junpeng Lao — I was the one who suggested doing a special episode about their new book, Bayesian Modeling and Computation in Python. And since they cannot say no to my soothing French accent, well, they didn’t say no…All of them were on the podcast already, so I’ll refer you to their solo episode for background on their background — aka backgroundception.Junpeng is a Data Scientist at Google, living in Zurich, Switzerland. Previously, he was a post-doc in Psychology and Cognitive Neuroscience. His current obsessions are time series and state space models. Osvaldo is a Researcher at CONICET in Argentina and the Department of Computer Science from Aalto University in Finland. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling.Ravin is a data scientist at Google, living in Los Angeles. Previously he worked at Sweetgreen and SpaceX. He became interested in Bayesian statistics when trying to quantify uncertainty in operations. He is especially interested in decision science in business settings.You’ll make your own opinion, but I like their book because uses a hands-on approach, focusing on the practice of applied statistics. And you get to see how to use diverse libraries, like PyMC, Tensorflow Probability, ArviZ, Bambi, and so on. You’ll see what I’m talking about in this episode.To top it off, the book is fully available online at bayesiancomputationbook.com. If you want a physical copy (because you love those guys and wanna support them), go to CRC website and enter the code ASA18 at checkout for a 30% discount.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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, 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, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland and Aubrey Clayton.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Website of the book: https://bayesiancomputationbook.com/welcome.htmlLBS #1 -- Bayes, open-source and bioinformatics, with Osvaldo Martin: https://www.learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martinOsvaldo on Twitter: https://twitter.com/aloctavodiaLBS #26 -- What you'll learn &amp; who you'll meet at the PyMC Conference, with Ravin Kumar &amp; Quan Nguyen: https://www.learnbayesstats.com/episode/26-what-youll-learn-who-youll-meet-at-the-pymc-conference-with-ravin-kumar-quan-nguyenRavin's blog: https://ravinkumar.com/Ravin on Twitter: https://twitter.com/canyon289LBS #7 -- Designing a Probabilistic Programming Language &amp; Debugging a Model, with Junpeng Lao: https://www.learnbayesstats.com/episode/7-designing-a-probabilistic-programming-language-debugging-a-model-with-junpeng-laoJunpeng on Twitter: https://twitter.com/junpenglaoMatchmaking Dinner #1, with Will Kurt and Junpeng Lao: https://www.patreon.com/posts/48360540Donate to PyMC: https://numfocus.org/pymc-bayesian-book-formDonate to ArviZ: https://numfocus.org/arviz-bayesian-book-formThis podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

You know when you have friends who wrote a book and pressure you to come on your podcast? That’s super annoying, right?Well that’s not what happened with <a href="https://twitter.com/canyon289" rel="noopener noreferrer nofollow" target="_blank">Ravin Kumar</a>, <a href="https://twitter.com/aloctavodia" rel="noopener noreferrer nofollow" target="_blank">Osvaldo Martin</a> and <a href="https://twitter.com/junpenglao" rel="noopener noreferrer nofollow" target="_blank">Junpeng Lao</a> — I was the one who suggested doing a special episode about their new book, <a href="https://bayesiancomputationbook.com/welcome.html" rel="noopener noreferrer nofollow" target="_blank">Bayesian Modeling and Computation in Python</a>. And since they cannot say no to my soothing French accent, well, they didn’t say no…All of them were on the podcast already, so I’ll refer you to their solo episode for background on their background — aka backgroundception.Junpeng is a Data Scientist at Google, living in Zurich, Switzerland. Previously, he was a post-doc in Psychology and Cognitive Neuroscience. His current obsessions are time series and state space models. Osvaldo is a Researcher at CONICET in Argentina and the Department of Computer Science from Aalto University in Finland. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling.Ravin is a data scientist at Google, living in Los Angeles. Previously he worked at Sweetgreen and SpaceX. He became interested in Bayesian statistics when trying to quantify uncertainty in operations. He is especially interested in decision science in business settings.You’ll make your own opinion, but I like their book because uses a hands-on approach, focusing on the practice of applied statistics. And you get to see how to use diverse libraries, like PyMC, Tensorflow Probability, ArviZ, Bambi, and so on. You’ll see what I’m talking about in this episode.To top it off, the book is fully available online at <a href="https://bayesiancomputationbook.com/welcome.html" rel="noopener noreferrer nofollow" target="_blank">bayesiancomputationbook.com</a>. If you want a physical copy (because you love those guys and wanna support them), go to CRC website and enter the code ASA18 at checkout for a 30% discount.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, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, 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, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland and Aubrey Clayton.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>Website of the book: <a href="https://bayesiancomputationbook.com/welcome.html" rel="noopener noreferrer nofollow" target="_blank">https://bayesiancomputationbook.com/welcome.html</a></li><li>LBS #1 -- Bayes, open-source and bioinformatics, with Osvaldo Martin: <a href="https://www.learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin" rel="noopener noreferrer nofollow" target="_blank">https://www.learnbayesstats.com/episode/1-bayes-open-source-and-bioinformatics-with-osvaldo-martin</a></li><li>Osvaldo on Twitter: <a href="https://twitter.com/aloctavodia" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/aloctavodia</a></li><li>LBS #26 -- What you'll learn &amp; who you'll meet at the PyMC Conference, with Ravin Kumar &amp; Quan Nguyen: <a href="https://www.learnbayesstats.com/episode/26-what-youll-learn-who-youll-meet-at-the-pymc-conference-with-ravin-kumar-quan-nguyen" rel="noopener noreferrer nofollow" target="_blank">https://www.learnbayesstats.com/episode/26-what-youll-learn-who-youll-meet-at-the-pymc-conference-with-ravin-kumar-quan-nguyen</a></li><li>Ravin's blog: <a href="https://ravinkumar.com/" rel="noopener noreferrer nofollow" target="_blank">https://ravinkumar.com/</a></li><li>Ravin on Twitter: <a href="https://twitter.com/canyon289" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/canyon289</a></li><li>LBS #7 -- Designing a Probabilistic Programming Language &amp; Debugging a Model, with Junpeng Lao: <a href="https://www.learnbayesstats.com/episode/7-designing-a-probabilistic-programming-language-debugging-a-model-with-junpeng-lao" rel="noopener noreferrer nofollow" target="_blank">https://www.learnbayesstats.com/episode/7-designing-a-probabilistic-programming-language-debugging-a-model-with-junpeng-lao</a></li><li>Junpeng on Twitter: <a href="https://twitter.com/junpenglao" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/junpenglao</a></li><li>Matchmaking Dinner #1, with Will Kurt and Junpeng Lao: <a href="https://www.patreon.com/posts/48360540" rel="noopener noreferrer nofollow" target="_blank">https://www.patreon.com/posts/48360540</a></li><li>Donate to PyMC: <a href="https://numfocus.org/pymc-bayesian-book-form" rel="noopener noreferrer nofollow" target="_blank">https://numfocus.org/pymc-bayesian-book-form</a></li><li>Donate to ArviZ: <a href="https://numfocus.org/arviz-bayesian-book-form" rel="noopener noreferrer nofollow" target="_blank">https://numfocus.org/arviz-bayesian-book-form</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>

#58 Bayesian Modeling and Computation, with Osvaldo Martin, Ravin Kumar and Junpeng Lao

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/junpenglaoJunpeng on GitHub: https://github.com/junpenglaoAdvanced Bayesian Modeling Tutorial: https://discourse.pymc.io/t/advance-bayesian-modelling-with-pymc3/1439Stan Devs' Prior Choice Recommendations: https://github.com/stan-dev/stan/wiki/Prior-Choice-RecommendationsPyMC Discourse: https://discourse.pymc.io/PyMC3 - Probabilistic Programming in Python: https://docs.pymc.io/Tensor Flow Probability: https://www.tensorflow.org/probability/This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

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 <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !Links from the show: <ul><li>Junpeng's blog: <a href="https://junpenglao.xyz/" rel="noopener noreferrer nofollow" target="_blank">https://junpenglao.xyz/</a></li><li>Junpeng on Twitter: <a href="https://twitter.com/junpenglao" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/junpenglao</a></li><li>Junpeng on GitHub: <a href="https://github.com/junpenglao" rel="noopener noreferrer nofollow" target="_blank">https://github.com/junpenglao</a></li><li>Advanced Bayesian Modeling Tutorial: <a href="https://discourse.pymc.io/t/advance-bayesian-modelling-with-pymc3/1439" rel="noopener noreferrer nofollow" target="_blank">https://discourse.pymc.io/t/advance-bayesian-modelling-with-pymc3/1439</a></li><li>Stan Devs' Prior Choice Recommendations: <a href="https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations" rel="noopener noreferrer nofollow" target="_blank">https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations</a></li><li>PyMC Discourse: <a href="https://discourse.pymc.io/" rel="noopener noreferrer nofollow" target="_blank">https://discourse.pymc.io/</a></li><li>PyMC3 - Probabilistic Programming in Python: <a href="https://docs.pymc.io/" rel="noopener noreferrer nofollow" target="_blank">https://docs.pymc.io/</a></li><li>Tensor Flow Probability: <a href="https://www.tensorflow.org/probability/" rel="noopener noreferrer nofollow" target="_blank">https://www.tensorflow.org/probability/</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>

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