Mean Field Simulation for Monte Carlo Integration

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A01=Pierre Del Moral
advanced particle algorithms
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Age Group_Uncategorized
applications of Monte Carlo simulation
Author_Pierre Del Moral
automatic-update
Boltzmann Gibbs Measures
Boltzmann Gibbs Transformation
Category1=Non-Fiction
Category=PBT
chain
COP=United States
Delivery_Pre-order
Elementary Transition
eq_isMigrated=2
Evolution Equation
feynman
Feynman Kac
Feynman Kac Measure
Feynman Kac Models
Feynman Kac Semigroups
Feynman-Kac particle models
Field Particle Model
Finite Constants
Function Fn
genetic particle algorithms
interacting particle methods
Ips Model
kac
Language_English
linear and nonlinear measure-valued processes
markov
Markov Chain
Markov Chain Model
Markov Transitions
mean field Monte Carlo particle algorithms
mean field particle simulation models
measurable
Measurable State Space
Measurable State Spaces En
measures
model
Monte Carlo integration and stochastic algorithms
Occupation Measure
Orlicz
Orlicz Norm
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Particle Absorption Models
Particle Density Profiles
Potential Functions Gn
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quantum and diffusion Monte Carlo methods
Random Fields
refined convergence analysis on nonlinear Markov chain models
Slutsky’s Lemma
softlaunch
spaces
state
stochastic perturbation analysis
transitions

Product details

  • ISBN 9781466504059
  • Weight: 1330g
  • Dimensions: 156 x 234mm
  • Publication Date: 20 May 2013
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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In the last three decades, there has been a dramatic increase in the use of interacting 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.

Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle simulation models and interdisciplinary research topics, including interacting jumps and McKean-Vlasov processes, sequential Monte Carlo methodologies, genetic particle algorithms, genealogical tree-based algorithms, and quantum and diffusion Monte Carlo methods.

Along with covering refined convergence analysis on nonlinear Markov chain models, the author discusses 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 population biology.

This book 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.

Pierre Del Moral is a professor in the School of Mathematics and Statistics at the University of New South Wales in Sydney, Australia.