Non-asymptotic Error Bounds for Sequential MCMC and Stability of Feynman-Kac Propagators

Abstract

We provide a generic way of deducing non-asymptotic error bounds for Sequential MCMC methods from suitable stability properties of Feynman-Kac propagators. We show how to derive this type of stability from mixing conditions for the MCMC dynamics, namely, spectral gaps and hyperboundedness, and from upper bounds on the relative densities in the sequence of distributions.

References in corpus (2)

• Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
• Quantitative bounds on convergence of time-inhomogeneous Markov chains

Cited by in corpus (4)

• Nonasymptotic analysis of adaptive and annealed Feynman-Kac particle models
• Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition
• Multilevel Sequential Monte Carlo Samplers for Normalizing Constants
• An Adaptive Sequential Monte Carlo Algorithm for Computing Permanents