Dr Michael Betancourt
Michael Betancourt is the principal 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.
Unravelling A Geometric Conspiracy
The Hamiltonian Monte Carlo method has proven a powerful approach to efficiently exploring complex probability distributions. That power, however, is something of a geometric conspiracy: a sequence of delicate mathematical machinations that ensure a means to explore distributions not just in theory but also in practice. In this talk I will discuss the coincident geometrical properties that ensure the scalable performance of Hamiltonian Monte Carlo and present recent work developing new geometric theories that generalize each of these properties individually, providing a foundation for generalizing the method without compromising its performance.