MCMC

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting

Daniel Paulin, Assistant Professor, School of Mathematics, University of Edinburgh.

Sep 20, 15:30 - 17:00

B1 L3 R3119

Monte Carlo MCMC

In this paper, we propose a detailed theoretical study of one of these algorithms known as the split Gibbs sampler. Under regularity conditions, we establish explicit convergence rates for this scheme using Ricci curvature and coupling ideas. We support our theory with numerical illustrations.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group

monte carlo algorithms uncertainty quantification multilevel methods data assimilation Inverse problem bayesian inference MCMC

Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

MCMC Particle Methods Stochastic Control machine learning stochastic partial differential equations