A sharp first order analysis of Feynman–Kac particle models, Part II: Particle Gibbs samplers
Langue
en
Article de revue
Ce document a été publié dans
Stochastic Processes and their Applications. 2018-01, vol. 128, n° 1, p. 354 - 371
Elsevier
Résumé en anglais
This article provides a new theory for the analysis of the particle Gibbs (PG) sampler (Andrieu et al., 2010). Following the work of Del Moral and Jasra (2017) we provide some analysis of the particle Gibbs sampler, giving ...Lire la suite >
This article provides a new theory for the analysis of the particle Gibbs (PG) sampler (Andrieu et al., 2010). Following the work of Del Moral and Jasra (2017) we provide some analysis of the particle Gibbs sampler, giving first order expansions of the kernel and minorization estimates. In addition, first order propagation of chaos estimates are derived for empirical measures of the dual particle model with a frozen path, also known as the conditional sequential Monte Carlo (SMC) update of the PG sampler. Backward and forward PG samplers are discussed, including a first comparison of the contraction estimates obtained by first order estimates. We illustrate our results with an example of fixed parameter estimation arising in hidden Markov models.< Réduire
Mots clés en anglais
Feynman–Kac formulae
Mean field particle models
Particle simulation
Particle Gibbs samplers
Propagation of chaos
Contraction inequalities
Dobrushin coefficients
Minorization conditions
Origine
Importé de halUnités de recherche