Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition

Ajay Jasra, Arnaud Doucet

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Sequential Monte Carlo (SMC) samplers [Del Moral, P., Doucet, A., Jasra, A., 2006. Sequential Monte Carlo samplers. J. Roy. Statist. Soc. B 68, 411-436] are designed to simulate from a sequence of probability measures on a common measurable space (E, E). One way to measure the accuracy of the resulting Monte Carlo estimates is the asymptotic variance in the central limit theorem (CLT). We investigate the conditions, for algorithms used in practice, which are sufficient to ensure that the resulting expression is upper bounded, of which, the typical conditions (e.g. [Chopin, N., 2004. Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference. Ann. Statist. 32, 2385-2411]) are quite restrictive. We use the Foster-Lyapunov condition and contractions in the f-norm of the Markov kernels [Douc, R., Moulines, E., Rosenthal, J.S., 2004. Quantitative bounds on convergence of time-inhomogeneous Markov chains. Ann. Appl. Probab. 14, 1643-1665] to establish quantitative bounds on the asymptotic variance. © 2008 Elsevier B.V. All rights reserved.
Original languageEnglish (US)
JournalStatistics and Probability Letters
Issue number17
StatePublished - Dec 1 2008
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2019-11-20


Dive into the research topics of 'Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition'. Together they form a unique fingerprint.

Cite this