The University of New South Wales
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 propagations 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.
Speaker Bio: Dr. Del Moral is one of the principal designers of the modern and the recently developing theory on stochastic particle methods in nonlinear filtering, numerical physics, engineering and information theory. He has published over 100 papers in pure and applied probability journals, and he is the author of the book "Feynman-Kac formulae. Genealogical and interacting particle approximations", Springer New York, Series: Probability and Applications (2004). His current research interests are : Bayesian inference and nonlinear filtering, multiple targets tracking problems, rare event analysis, calibration and uncertainty propagations in numerical codes, particle absorption models, Monte Carlo methods, stochastic algorithms, branching processes and interacting particle systems. Since 2007, Pierre Del Moral is a joint senior research fellow at INRIA, and at the Mathematical Institute in Bordeaux. In 2011, he also joined the Applied Mathematical Center of the Polytechnique School in Paris as a Professor "chargé de cours". After a masters degree in pure mathematics in 1989 in the University Paul Sabatier in Toulouse, a PhD in 1994 in signal processing with one of the first study on stochastic particle methods in nonlinear filtering and optimal control problems in 1995, he joined the C.N.R.S. as a junior research fellow in mathematics and physics at the Probability and Statistical department of the University Paul Sabatier in Toulouse, and he received in 2002 the higher degree of research (H.D.R.) in Mathematics. In 2004, he joined the Lab. J. A. Dieudonné of the University of Nice and Sophia-Antipolis as a full Professor of Mathematics in the field of Probability and stochastic processes. He has also been a visiting professor in the Russian academy of sciences as well as in several international universities, including Beijing, Cambridge, Edmonton, Erlangen, La Havana, Helsinki, Melbourne, Montréal, Moscow, St Petersbourg, Sydney, Tokyo, Oxford, Princeton, Purdue, and Wuhan University. A printable flyer is available at http://www.statsoc.org.au/objectlibrary/1417?filename=SSAIAGS-DelMoral-Flyer.pdf. This notice will be available shortly on the AMSI website (www.amsi.org.au): Events > AGR Events
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