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SUMMARY:On the uniform ergodicity of the particle Gibbs sampler - Moulines
 \, E (Tlcom ParisTech)
DTSTART:20140422T100500Z
DTEND:20140422T104000Z
UID:TALK52090@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-authors: Randal Douc (Telecom SudParis)\, Fred Lindsten (Ca
 mbridge) \n\nThe particle Gibbs sampler is a systematic way of using a par
 ticle filter within Markov chain Monte Carlo (MCMC). This results in an of
 f-the-shelf Markov kernel on the space of state trajectories\, which can b
 e 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 unifor
 mly ergodic under rather general assumptions\, that we will carefully revi
 ew 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 k
 ept constant by increasing the number of particles superlinearly with the 
 number of observations. We illustrate the applicability of our result by s
 tudying in detail two common state space models with non-compact state spa
 ces. \n
LOCATION:Seminar Room 1\, Newton Institute
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