Michael Betancourt is an experimental physicist and applied statistician focusing on the broad-scale applicability of bayesian reasoning in science.
He works as an independent consultant, and he is known as one of the core developers of Stan, a probabilistic programming language for statistical inference.We start talking about his physics background and his passion for Bayesian reasoning. We then talk about the challenges and opportunities of conducting research outside academia, from the necessity of alternative means of funding to the intellectual freedom gained outside institutions, being able to make everything freely accessible for example. We then introduce Bayesian inference and the potential of the Stan language in a number of scientific fields. We go inside what Stan does, talking about Markov Chain Monte Carlo, and focusing in particular on Hamiltonian Monte Carlo and on the good properties of symplectic geometry. We talk about the relations with convexification methods and the limitation of the use of Hilbert space methods and Machine Learning techniques. We close briefly touching on the Poincaré recurrence theorem, Hamiltonian chaos, and Geometric Ergodicity.

LINKS:
https://betanalpha.github.io/
https://www.patreon.com/betanalpha/
https://twitter.com/betanalpha/

RESOURCES:
Anchor: https://anchor.fm/poincare-podcast
Youtube: https://www.youtube.com/watch.v
RSS: https://anchor.fm/s/84561ce0/podcast/rss
Linktree: https://linktr.ee/poincaretrajectories
Company: https://www.linkedin.com/company/poincaretrajectories/