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

• Published in: Statistics & probability letters Vol. 78; no. 17; pp. 3062 - 3069
• Main Authors: Jasra, Ajay, Doucet, Arnaud
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2008; Elsevier
• Series: Statistics & Probability Letters
• Subjects: Distribution theory; Exact sciences and technology; Experimental design; General topics; Mathematics; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Statistics; Monte Carlo method; Variance estimation; Probability theory; Asymptotic variance; Algorithm; Convergence; Kernel method; Markov chain; Statistical method; Kernels; Upper bound; Central limit theorem; Sequential method; Experimental design; Sampler; Probability measure; Numerical stability
• ISSN: 0167-7152; 1879-2103
• DOI: 10.1016/j.spl.2008.05.023

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.