  Statisticians often use Monte Carlo methods to approximate probability
distributions, primarily with Markov chain Monte Carlo and importance sampling.
Sequential Monte Carlo samplers are a class of algorithms that combine both
techniques to approximate distributions of interest and their normalizing
constants. These samplers originate from particle filtering for state space
models and have become general and scalable sampling techniques. This article
describes sequential Monte Carlo samplers and their possible implementations,
arguing that they remain under-used in statistics, despite their ability to
perform sequential inference and to leverage parallel processing resources
among other potential benefits.
