Fast Sequential Monte Carlo Methods for Counting and Optimization

Written by authorities in the field, this book presents an introduction to fast sequential Monte Carlo (SMC) methods for counting and optimization. Fast Sequential Monte Carlo Methods for Counting and Optimiztion is based on many years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, for counting problems, and for combinatorial optimization. Particular emphasis throughout the book is placed on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. The overall aim is to make SMC methods accessible to readers who want to apply and to accentuate the unifying and novel mathematical ideas behind SMC in their future studies or work. Formal definitions (e.g. theorems and proofs) are embedded either in-text or in examples and experiments while case studies are emphasized when and where appropriate.

Keywords: Applied Probability & Statistics, sequential Monte Carlo, SMC, mathematics, statistics, computer science, combinatorial optimization, machine learning, communication networks, secure protocols, Monte Carlo Methods, MCM, engineering, applied statistics, cross-entropy, minimum cross-entropy, stochastic enumeration