An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo These methods are capable of modeling nonlinear systems, such as those for computer vision, and pollution monitoring, that cannot be satisfactorily handled by classical techniques.
Olivier Cappé, Simon Godsill, Éric Moulines · Proceedings of the IEEE · 2007
It is now over a decade since the pioneering con- tribution of Gordon et al. (1993), which is commonly regarded as the first instance of modern sequential Monte Carlo (SMC) approaches. Initially focussed on applications to tracking and vision, these techniques are now very widespread and have had a significant impact in virtually all areas of signal and image processing concerned with Bayesian dynamical models. This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.