Statistics Seminar: Pierre Del-Moral -- Particle Monte Carlo methods in statistical learning and rare event simulation

Abstract:

In the last three decades, there has been a dramatic increase in 
the use of particle methods as a powerful tool in real-world 
applications of Monte Carlo simulation in computational physics, 
population biology, computer sciences, and statistical machine 
learning. Ideally suited to parallel and distributed computation, 
these advanced particle algorithms include nonlinear interacting 
jump diffusions; quantum, diffusion, and resampled Monte Carlo 
methods; Feynman-Kac particle models; genetic and evolutionary 
algorithms; sequential Monte Carlo methods; adaptive and 
interacting Markov chain Monte Carlo models; bootstrapping 
methods; ensemble Kalman filters; and interacting particle 
filters.

This lecture presents a comprehensive treatment of mean field 
particle simulation models and interdisciplinary research topics, 
including sequential Monte Carlo methodologies, genetic particle 
algorithms, genealogical tree-based algorithms, and quantum and 
diffusion Monte Carlo methods.

Along with covering refined convergence analysis of particle 
algorithms, we also discuss applications related to parameter 
estimation in hidden Markov chain models, stochastic optimization, 
nonlinear filtering and multiple target tracking, stochastic 
optimization, calibration and uncertainty propagation in numerical 
codes, rare event simulation, financial mathematics, and free 
energy and quasi-invariant measures arising in computational 
physics and dynamic population biology.

This presentation shows how mean field particle simulation has 
revolutionized the field of Monte Carlo integration and stochastic 
algorithms. It will help theoretical probability researchers, 
applied statisticians, biologists, statistical physicists, and 
computer scientists work better across their own disciplinary 
boundaries.