sequential Monte Carlo

Bayesian inference as optimal transportation By Prof. Youssef Marzouk Massachusetts Institute of Technology, USA

Prof. Youssef Marzouk, Massachusetts Institute of Technology, USA

Apr 22, 14:00 - 15:00

B1 R4214

bayesian inference sequential Monte Carlo

Bayesian inference provides a natural framework for quantifying uncertainty in parameter estimates and model predictions, for combining heterogeneous sources of information, and for conditioning sequential predictions on data. Posterior simulation in Bayesian inference often proceeds via Markov chain Monte Carlo (MCMC) or sequential Monte Carlo (SMC), but the associated computational expense and convergence issues can present significant bottlenecks in large-scale or dynamically complex problems.