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Also appears in collection : Jean-Morlet chair: Masterclass in Bayesian statistics / Chaire Jean-Morlet : École de statistique bayésienne

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$^2$.

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Citation data

  • DOI 10.24350/CIRM.V.19468603
  • Cite this video Chopin Nicolas (10/25/18). An introduction to particle filters. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19468603
  • URL https://dx.doi.org/10.24350/CIRM.V.19468603

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