McKean-Vlasov SDEs

Bayesian Inference for Partially Observed Continuous-Time Processes

Amin Wu, Ph.D. Student, Statistics

Mar 3, 10:00 - 12:00

B5 L5 R5220

McKean-Vlasov SDEs bayesian inference markov chains Monte Carlo

This thesis develops Bayesian inference methods for partially observed stochastic differential equations (SDEs) with unknown parameters, focusing on the stochastic Volterra equation (SVE), non-synchronous diffusions, and McKean-Vlasov SDEs. Employing Euler-Maruyama discretization.

Numerical approximation of McKean-Vlasov SDEs via Stochastic Gradient Descent

Prof. Gonçalo dos Reis, School of Mathematics, University of Edinburgh

Nov 1, 15:30 - 17:00

B1 L3 R3119

stochastic gradient descent McKean-Vlasov SDEs

We propose a novel approach of numerically approximate McKean-Vlasov SDEs that avoids the usual interacting particle approximation and Propagation of Chaos results altogether.