Mean Field Simulation for Monte Carlo Integration

✍ Scribed by Pierre Del Moral

• Publisher: Taylor and Francis
• Year: 2013
• Tongue: English
• Leaves: 624
• Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
• Category: Library

✦ Synopsis

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 Marko. Read more...

✦ Table of Contents

Content: Front Cover; Dedication; Contents; Preface; Frequently used notation; Part I --
Introduction; Chapter 1 --
Monte Carlo and mean field models; Chapter 2 --
Theory and applications; Part II --
Feynman-Kac models; Chapter 3 --
Discrete time Feynman-Kac models; Chapter 4 --
Four equivalent particle interpretations; Chapter 5 --
Continuous time Feynman-Kac models; Chapter 6 --
Nonlinear evolutions of intensity measures; Part III --
Application domains; Chapter 7 --
Particle absorption models; Chapter 8 --
Signal processing and control systems; Part IV --
Theoretical aspects. Chapter 9 --
Mean field Feynman-Kac modelsChapter 10 --
A general class of mean field models; Chapter 11 --
Empirical processes; Chapter 12 --
Feynman-Kac semigroups; Chapter 13 --
Intensity measure semigroups; Chapter 14 --
Particle density profiles; Chapter 15 --
Genealogical tree models; Chapter 16 --
Particle normalizing constants; Chapter 17 --
Backward particle Markov models; Bibliography; Back Cover.
Abstract: 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 Marko