Time and place: 16:00 on Zoom. Register online!

Speaker: Nikola Surjanovic, University of British Columbia

Title: Software and MCMC methods for sampling from complex distributions

Abstract: We introduce a software package, Pigeons.jl, that provides a way to leverage distributed computation to obtain samples from complicated probability distributions, such as multimodal posteriors arising in Bayesian inference and high-dimensional distributions in statistical mechanics. Pigeons.jl provides simple APIs to perform such computations single-threaded, multi-threaded, and/or distributed over thousands of MPI-communicating machines. Our software provides several Markov kernels, including our newly proposed algorithm, autoMALA. This MCMC algorithm, based on the Metropolis-adjusted Langevin algorithm, automatically sets its step size at each iteration based on the local geometry of the target distribution. Our experiments demonstrate that autoMALA is competitive with related state-of the-art MCMC methods, in terms of the number of log density evaluations per effective sample, and it outperforms state-of-the-art samplers on targets with varying geometries.

Bio: Nikola Surjanovic is a Vanier Scholar pursuing a PhD in Statistics at the University of British Columbia under the supervision of Dr. Alexandre Bouchard-Côté and Dr. Trevor Campbell. His research interests include scalable Bayesian inference and machine learning. Nikola is also a core contributor and founding member of the Pigeons software project for distributed sampling from difficult distributions and for solving computational Lebesgue integration problems.