SMS scnews item created by John Ormerod at Tue 25 Mar 2014 0942
Type: Seminar
Distribution: World
Expiry: 1 Apr 2014
Calendar1: 28 Mar 2014 1400-1500
CalLoc1: Carslaw 173
Auth: jormerod@pjormerod4.pc (assumed)

# 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.


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