Also appears in collection : Research School

This course will give a gentle introduction to SMC (Sequential Monte Carlo algorithms): • motivation: state-space (hidden Markov) models, sequential analysis of such models; non-sequential problems that may be tackled using SMC. • Formalism: Markov kernels, Feynman-Kac distributions. • Monte Carlo tricks: importance sampling and resampling • standard particle filters: bootstrap, guided, auxiliary • maximum likelihood estimation of state-stace models • Bayesian estimation of these models: PMCMC, SMC$^2$.