The sequential Monte Carlo (SMC) methodology has shown a great promise in solving a large class of highly complex inference and optimization problems, opening up new frontiers for cross-fertilization between statistical science and many application areas.

SMC can be loosely defined as a family of techniques that use Monte Carlo simulations to solve high dimensional inference and prediction problems. For problems with fixed dimension, it serves as a useful alternative to the popular MCMC algorithms. In stochastic dynamic systems it provides a unique and powerful tool for on-line estimation and prediction problems. By recursively generating random samples of the state variables, SMC adapts flexibly to the dynamics of the underlying stochastic systems. In this talk, we present an overview of the current status of SMC, its applications and some recent developments. Specifically, we will introduce a general framework of SMC, and discuss various strategies on fine-tuning the different components in the SMC algorithm, in order to achieve maximum efficiency. SMC applications, especially those in science, engineering, bioinformatics and financial data analysis will be discussed.