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SUMMARY:Nelder Fellow Lecture Series – Prof Pierre Del Moral: Advanced Mo
nte Carlo methods
DESCRIPTION:This course will cover topics in the general area of Monte Carl
o methods and their application domains. The topics include Markov chain M
onte Carlo and Sequential Monte Carlo methods\, Genetic algorithms\, Quant
um and Diffusion Monte Carlo techniques\, as well as branching and interac
ting particle methodologies. The lectures cover discrete and continuous ti
me stochastic models\, starting from traditional sampling techniques (perf
ect simulation\, Metropolis-Hasting\, and Gibbs-Glauber models) to more re
fined methodologies such as self interacting and mean field type Interacti
ng Particle Systems methodologies. To name a few\, forward/backward partic
le filters\, Ensemble Kalman filers\, interacting Kalman filters\, Sequent
ial Monte Carlo\, genealogical tree based samplers\, particle Gibbs and pa
rticle Metropolis-Hastings\, interacting Metropolis-Hastings\, multiple-le
vel splitting\, and many others.\nThe course offers a pedagogical introduc
tion to the theoretical foundations of these advanced stochastic models\,
combined with a series of concrete illustrations taken from different appl
ication domains. The applications considered in these lectures will range
from Bayesian statistical learning (hidden Markov chain\, statistical mach
ine learning)\, risk analysis and rare event sampling (mathematical financ
e\, and industrial risk assessment)\, operation research (global optimizat
ion\, combinatorial counting and ranking)\, advanced signal processing (st
ochastic nonlinear filtering and control and data association)\, computati
onal and statistical physics (Feynman-Kac formulae on path spaces\, molecu
lar dynamics\, Schrodinger’s ground states\, Boltzmann-Gibbs distributio
ns\, and free energy computation). Some textbooks which can be useful for
supplemental reading are: \n\n Stochastic Processes: From Applications to
Theory. P. Del Moral\, & S. Penev Chapman and Hall/CRC (2016).\nMean fiel
d simulation for Monte Carlo integration. P. Del Moral. Chapman & Hall/CRC
Monographs on Statistics & Applied Probability (2013).\n Feynman-Kac for
mulae. Genealogical and interacting particle approximations. P. Del Moral.
Springer New York. Series: Probability and Applications (2004).\nBranchin
g and Interacting Particle Systems Approximations of Feynman-Kac Formulae.
P. Del Moral & L. Miclo (2000). Seminaire de Probabilities\, Lecture Notes
in Mathematics.\nFundamentals of Stochastic Filtering. A. Bain and D. Cri
san. Springer\, Stochastic Modelling and Applied Probability\, Vol. 60 (20
09).\nInference in Hidden Markov Models. O. Capp_e\, E. Moulines\, and T.
Ryden. Springer series in Statistics (2005).\nAn Introduction to Sequentia
l Monte Carlo. N. Chopin \, O. Papaspiliopoulos\, Springe Series in Statis
tics (2020).\n\nThe lectures will be held as below:\nWed 13 March 1pm-3pm\
nWed 20 March 1pm-3pm\nThu 21 March 10am-12pm\nWed 27 March 1pm-3pm\n\nSho
rt bio:\nSince 2007\, Pierre Del Moral is a Research Director (first class
since 2011) at INRIA.\nIn 2014-2016\, he was Professor at the School of M
athematics and Statistics of the University of New South Wales in Sydney\,
Australia.\nIn 2011-2014\, he also joined the Applied Mathematical Center
of the Polytechnique School in Paris as a Professor “charge de cours”
. After a masters degree in pure mathematics in 1989 in the University Pau
l Sabatier in Toulouse in the field of Cohomology\, Dynamical Systems\, Hy
perbolic Geometry and Algebraic Geometry\, he joined the LAAS Automation a
nd Control Institute of the C.N.R.S. (Centre National de la Recherche Scie
ntifique). He obtained a PhD in 1994 in signal processing with one of the
first study on stochastic particle methods in nonlinear filtering and opti
mal control problems. From 1992 to 1995\, he also served as a lecturer in
mathematics at the “Ecole Nationale Superieure de l’Aeronautique et de
l’Espace”\, and as a research engineer in the company Steria-Digilog\
, working on particle filters in tracking problems arising in radar and so
nar signal processing problems.\nIn 1995\, he joined the C.N.R.S. as a jun
ior research fellow in mathematics and physics at the Probability and Stat
istical department of the University Paul Sabatier in Toulouse\, and he re
ceived in 2002 the higher degree of research (H.D.R.) in Mathematics. In 2
004\, he joined the Lab. J. A. Dieudonne of the University of Nice and Sop
hia-Antipolis as a full Professor of Mathematics in the field of Probabili
ty and stochastic processes. He has also been a visiting professor in the
russian academy of sciences as well as in several international universiti
es\, including Beijing\, Cambridge\, Edmonton\, Erlangen\, La Havana\, Hel
sinki\, Melbourne\, Montreal\, Moscow\, St Petersbourg\, Sydney\, Tokyo\,
Oxford\, Princeton\, Purdue\, and Wuhan University.\nPr. Del Moral is one
of the principal designers of the modern and the recently developing theor
y on stochastic particle methods in nonlinear filtering\, numerical physic
s\, engineering and information theory. He has published over 200 papers i
n pure and applied probability journals\, and he is the author of the book
s “Mean field simulation for Monte Carlo integration”\, Chapman and Ha
ll/CRC Press\, monographs on Stats and Applied Probability (2013)\, and
“Feynman-Kac formulae. Genealogical and interacting particle approximati
ons”\, Springer New York\, Series: Probability and Applications (2004).
His current research interests are : bayesian inference and nonlinear filt
ering\, multiple targets tracking problems\, rare event analysis\, calibra
tion and uncertainty propagations in numerical codes\, particle absorption
models\, Monte Carlo methods\, stochastic algorithms\, branching processe
s and interacting particle systems.\n
URL:https://www.imperial.ac.uk/events/173350/nelder-fellow-lecture-series-p
rof-pierre-del-moral-4/
DTSTART;TZID=Europe/London:20240327T130000
DTEND;TZID=Europe/London:20240327T150000
LOCATION:tbc\, Huxley Building\, South Kensington Campus\, Imperial College
London\, London\, SW7 2AZ\, United Kingdom
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