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Article Dans Une Revue Scandinavian Journal of Statistics Année : 2015

Uniform ergodicity of the Particle Gibbs sampler

Résumé

The particle Gibbs (PG) sampler is a systematic way of using a particle filter within Markov chain Monte Carlo (MCMC). This results in an off-the-shelf Markov kernel on the space of state trajectories, which can be used to simulate from the full joint smoothing distribution for a state space model in an MCMC scheme. We show that the PG Markov kernel is uniformly ergodic under rather general assumptions, that we will carefully review and discuss. In particular, we provide an explicit rate of convergence which reveals that: (i) for fixed number of data points, the convergence rate can be made arbitrarily good by increasing the number of particles, and (ii) under general mixing assumptions, the convergence rate can be kept constant by increasing the number of particles superlinearly with the number of observations. We illustrate the applicability of our result by studying in detail two common state space models with non-compact state spaces

Dates et versions

hal-01257066 , version 1 (15-01-2016)

Identifiants

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Fredrik Lindsten, Randal Douc, Éric Moulines. Uniform ergodicity of the Particle Gibbs sampler. Scandinavian Journal of Statistics, 2015, 42 (3), pp.775 - 797. ⟨10.1111/sjos.12136⟩. ⟨hal-01257066⟩
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