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#54 Bayes in Theoretical Ecology, with Florian Hartig

#54 Bayes in Theoretical Ecology, with Florian Hartig

FromLearning Bayesian Statistics


#54 Bayes in Theoretical Ecology, with Florian Hartig

FromLearning Bayesian Statistics

ratings:
Length:
69 minutes
Released:
Jan 14, 2022
Format:
Podcast episode

Description

Let’s be honest: evolution is awesome! I started reading Improbable Destinies: Fate, Chance, and the Future of Evolution, by Jonathan Losos, and I’m utterly fascinated. 
So I’m thrilled to welcome Florian Hartig on the show. Florian is a professor of Theoretical Ecology at the University of Regensburg, Germany. His research concentrates on theory, computer simulations, statistical methods and machine learning in ecology & evolution. He is also interested in open science and open software development, and maintains, among other projects, the R packages DHARMa and BayesianTools.
Among other things, we talked about approximate Bayesian computation, best practices when building models and the big pain points that remain in the Bayesian pipeline.
Most importantly, Florian’s main hobbies are whitewater kayaking, snowboarding, badminton and playing the guitar.
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, 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 and Daniel Lindroth.
Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Florian's website: https://theoreticalecology.wordpress.com/ (https://theoreticalecology.wordpress.com/)
Florian on Twitter: https://twitter.com/florianhartig (https://twitter.com/florianhartig)
Florian on GitHub: https://github.com/florianhartig (https://github.com/florianhartig)
DHARMa -- Residual Diagnostics for Hierarchical Regression Models: https://cran.r-project.org/web/packages/DHARMa/index.html (https://cran.r-project.org/web/packages/DHARMa/index.html)
BayesianTools -- General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics: https://cran.r-project.org/web/packages/BayesianTools/index.html (https://cran.r-project.org/web/packages/BayesianTools/index.html)
Statistical inference for stochastic simulation inference -- theory and application: https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1461-0248.2011.01640.x (https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1461-0248.2011.01640.x)
ArviZ plot rank function: https://arviz-devs.github.io/arviz/api/generated/arviz.plot_rank.html (https://arviz-devs.github.io/arviz/api/generated/arviz.plot_rank.html)
Rank-normalization, folding, and localization -- An improved R-hat for assessing convergence of MCMC: https://arxiv.org/abs/1903.08008 (https://arxiv.org/abs/1903.08008)
LBS #51 Bernoulli's Fallacy & the Crisis of Modern Science, with Aubrey Clayton: https://www.learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton (https://www.learnbayesstats.com/episode/51-bernoullis-fallacy-crisis-modern-science-aubrey-clayton)
LBS #50 Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://www.learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter...
Released:
Jan 14, 2022
Format:
Podcast episode

Titles in the series (100)

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 https://www.pymc-labs.io/ (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 https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (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 https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy