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 par

part 1|2 pages

Part I - Introduction

chapter 1|34 pages

- Monte Carlo and mean field models

chapter 2|40 pages

- Theory and applications

part 2|2 pages

Part II - Feynman-Kac models

chapter 3|32 pages

- Discrete time Feynman-Kac models

chapter 4|36 pages

- Four equivalent particle interpretations

chapter 5|18 pages

- Continuous time Feynman-Kac models

part 3|2 pages

Part III - Application domains

chapter 7|24 pages

- Particle absorption models

chapter 8|42 pages

- Signal processing and control systems

part 4|2 pages

Part IV - Theoretical aspects

chapter 9|28 pages

- Mean field Feynman-Kac models

chapter 10|28 pages

- A general class of mean field models

chapter 11|42 pages

- Empirical processes

chapter 12|22 pages

- Feynman-Kac semigroups

chapter 13|34 pages

- Intensity measure semigroups

chapter 14|44 pages

- Particle density profiles

chapter 15|14 pages

- Genealogical tree models

chapter 16|18 pages

- Particle normalizing constants

chapter 17|30 pages

- Backward particle Markov models