#54 Bayes in Theoretical Ecology, with Florian Hartig

Learning Bayesian Statistics

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/ !

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 to unlock exclusive Bayesian swag ;)

Links from the show:

• Florian's website: https://theoreticalecology.wordpress.com/
• Florian on Twitter: https://twitter.com/florianhartig
• Florian on GitHub: https://github.com/florianhartig
• DHARMa -- Residual Diagnostics for Hierarchical Regression Models: 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
• Statistical inference for stochastic simulation inference -- theory and application: 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
• Rank-normalization, folding, and localization -- An improved R-hat for assessing convergence of MCMC: 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
• LBS #50 Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter: https://www.learnbayesstats.com/episode/50-talking-risks-embracing-uncertainty-david-spiegelhalter
• LBS #44 Building Bayesian Models at scale, with Rémi Louf: https://www.learnbayesstats.com/episode/44-bayesian-models-at-scale-remi-louf
• LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://www.learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner
• LBS #29 Model Assessment, Non-Parametric Models, And Much More, with Aki Vehtari: https://www.learnbayesstats.com/episode/model-assessment-non-parametric-models-aki-vehtari
• Improbable Destinies -- Fate, Chance, and the Future of Evolution: https://www.goodreads.com/book/show/33357463-improbable-destinies