Abstract: 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 Michael Betancourt will present a conceptual
discussion of the challenges inherent to Bayesian computation and the
foundations of why Hamiltonian Monte Carlo in uniquely suited to
surmount them.
Biography: 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.
Website: https://betanalpha.github.io
Twitter: @betanalpha