#90, Demystifying MCMC & Variational Inference, with Charles Margossian
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https://www.pymc-labs.io/
My Intuitive Bayes Online Courses: https://www.intuitivebayes.com/
1:1 Mentorship with me: https://topmate.io/alex_andorra
What’s the difference between MCMC and Variational Inference (VI)? Why is MCMC called an approximate method? When should we use VI instead of MCMC?
These are some of the captivating (and practical) questions we’ll tackle in this episode. I had the chance to interview Charles Margossian, a research fellow in computational mathematics at the Flatiron Institute, and a core developer of the Stan software.
Charles was born and raised in Paris, and then moved to the US to pursue a bachelor’s degree in physics at Yale university. After graduating, he worked for two years in biotech, and went on to do a PhD in statistics at Columbia University with someone named… Andrew Gelman — you may have heard of him.
Charles is also specialized in pharmacometrics and epidemiology, so we also talked about some practical applications of Bayesian methods and algorithms in these fascinating fields.
Oh, and Charles’ life doesn’t only revolve around computers: he practices ballroom dancing and pickup soccer, and used to do improvised musical comedy!
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, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar and Matt Rosinski.
Visit Patreon: learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
Charles’ website: https://charlesm93.github.io/
Charles on Twitter: Twitter: charlesm993
Charles on GitHub: https://github.com/charlesm93
Charles on Google Scholar: https://scholar.google.com/citations?...
Stan software: https://mc-stan.org/
Torsten – Applications of Stan in Pharmacometrics: https://github.com/metrumresearchgrou...
R̂ – Assessing the convergence of Markov chain Monte Carlo when running many short chains: https://arxiv.org/abs/2110.13017
Revisiting the Gelman-Rubin Diagnostic: https://arxiv.org/abs/1812.09384
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models: https://arxiv.org/abs/2205.09059
Pathfinder – Parallel quasi-Newton variational inference: https://arxiv.org/pdf/2108.03782.pdf
Bayesian workflow for disease transmission modeling in Stan: https://mc-stan.org/users/documentati...
LBS #76 – The Past, Present & Future of Stan, with Bob Carpenter: https://learnbayesstats.com/episode/7...
LBS #51 – Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/5...
Flatiron Institute: https://www.simonsfoundation.org/flat...
Simons Foundation: https://www.simonsfoundation.org/
Timestamps
00:00:00 Episode starts
00:04:10 How did you come to the world of statistics, pharmacometrics ..
00:15:06 Variation on inference underestimates uncertainty ..
00:23:43 MCMC is an exact method, whereas VI remains approximate ..
00:31:36 When would VI be more helpful in comparison to MCMC?
00:41:19 What are the frontiers currently in the field of algorithms that you ..
00:51:54 Making compromises
01:05:47 Do you have an example of a research project where you applied Bayesian statistics
01:26:18 How do you choose the prior when you are dealing with complex models?
01:37:50 What resources or strategies do you recommend to those who want to learn Bayesian stats
https://www.pymc-labs.io/
My Intuitive Bayes Online Courses: https://www.intuitivebayes.com/
1:1 Mentorship with me: https://topmate.io/alex_andorra
What’s the difference between MCMC and Variational Inference (VI)? Why is MCMC called an approximate method? When should we use VI instead of MCMC?
These are some of the captivating (and practical) questions we’ll tackle in this episode. I had the chance to interview Charles Margossian, a research fellow in computational mathematics at the Flatiron Institute, and a core developer of the Stan software.
Charles was born and raised in Paris, and then moved to the US to pursue a bachelor’s degree in physics at Yale university. After graduating, he worked for two years in biotech, and went on to do a PhD in statistics at Columbia University with someone named… Andrew Gelman — you may have heard of him.
Charles is also specialized in pharmacometrics and epidemiology, so we also talked about some practical applications of Bayesian methods and algorithms in these fascinating fields.
Oh, and Charles’ life doesn’t only revolve around computers: he practices ballroom dancing and pickup soccer, and used to do improvised musical comedy!
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, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar and Matt Rosinski.
Visit Patreon: learnbayesstats to unlock exclusive Bayesian swag ;)
Links from the show:
Charles’ website: https://charlesm93.github.io/
Charles on Twitter: Twitter: charlesm993
Charles on GitHub: https://github.com/charlesm93
Charles on Google Scholar: https://scholar.google.com/citations?...
Stan software: https://mc-stan.org/
Torsten – Applications of Stan in Pharmacometrics: https://github.com/metrumresearchgrou...
R̂ – Assessing the convergence of Markov chain Monte Carlo when running many short chains: https://arxiv.org/abs/2110.13017
Revisiting the Gelman-Rubin Diagnostic: https://arxiv.org/abs/1812.09384
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models: https://arxiv.org/abs/2205.09059
Pathfinder – Parallel quasi-Newton variational inference: https://arxiv.org/pdf/2108.03782.pdf
Bayesian workflow for disease transmission modeling in Stan: https://mc-stan.org/users/documentati...
LBS #76 – The Past, Present & Future of Stan, with Bob Carpenter: https://learnbayesstats.com/episode/7...
LBS #51 – Bernoulli’s Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://learnbayesstats.com/episode/5...
Flatiron Institute: https://www.simonsfoundation.org/flat...
Simons Foundation: https://www.simonsfoundation.org/
Timestamps
00:00:00 Episode starts
00:04:10 How did you come to the world of statistics, pharmacometrics ..
00:15:06 Variation on inference underestimates uncertainty ..
00:23:43 MCMC is an exact method, whereas VI remains approximate ..
00:31:36 When would VI be more helpful in comparison to MCMC?
00:41:19 What are the frontiers currently in the field of algorithms that you ..
00:51:54 Making compromises
01:05:47 Do you have an example of a research project where you applied Bayesian statistics
01:26:18 How do you choose the prior when you are dealing with complex models?
01:37:50 What resources or strategies do you recommend to those who want to learn Bayesian stats
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در تاریخ 1402/06/15 منتشر شده
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