Sequential Monte Carlo methods and their applications

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

The sequential Monte Carlo (SMC) methodology is a family of Monte Carlo methods that processes information sequentially. It has shown to be able to solve a large class of highly complex inference and optimization problems that can be formulated as stochastic dynamic systems. By recursively generating random samples of the state variables of the dynamic systems, SMC adapts flexibly to the dynamics of the underlying stochastic systems. It opens up new frontiers for cross-fertilization between statistical science and many application areas. In this note, we present an overview of SMC, its applications and some recent developments. Specifically, we introduce a general framework of SMC, and discuss various strategies on fine-tuning the different components in the SMC framework in order to achieve maximum efficiency. SMC applications, specially those in science, engineering, bioinformatics and financial data analysis are discussed.

Original languageEnglish (US)
Title of host publicationMarkov Chain Monte Carlo
Subtitle of host publicationInnovations And Applications
PublisherWorld Scientific Publishing Co.
Pages147-182
Number of pages36
ISBN (Electronic)9789812700919
DOIs
StatePublished - Jan 1 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

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