Michael Betancourt: Scalable Bayesian Inference with Hamiltonian Monte Carlo

Recording of Michael Betancourt's talk at the London Machine Learning Meetup: https://www.meetup.com/London-Machine-Learning-Meetup/events/251198486/

Despite the promise of big data, inferences are often limited not by sample size but rather by systematic effects. Only by carefully modeling these effects can we take full advantage of the data -- big data must be complemented with big models and the algorithms that can fit them. One such algorithm is Hamiltonian Monte Carlo, which exploits the inherent geometry of the posterior distribution to admit full Bayesian inference that scales to the complex models of practical interest. In this talk, I will discuss the conceptual foundations of Hamiltonian Monte Carlo, elucidating the geometric nature of its scalable performance and stressing the properties critical to a robust implementation.

Bio: Michael Betancourt is the principle research scientist with Symplectomorphic, LLC where he develops theoretical and methodological tools to support practical Bayesian inference. He is also a core developer of Stan, where he implements and tests these tools. In addition to hosting tutorials and workshops on Bayesian inference with Stan, he also collaborates on analyses in epidemiology, pharmacology, and physics, amongst others. Before moving into statistics, Michael earned a B.S. from the California Institute of Technology and a Ph.D. from the Massachusetts Institute of Technology, both in physics.

https://evolution.ai/ : Machines that Read - Intelligent data extraction from corporate and financial documents.

Видео Michael Betancourt: Scalable Bayesian Inference with Hamiltonian Monte Carlo канала London Machine Learning Meetup