Quantitative approximations of evolving probability measures and sequential Markov chain Monte Carlo methods
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We study approximations of evolving probability measures by an interacting particle system. The particle system dynamics is a combination of independent Markov chain moves and importance sampling/resampling steps. Under global regularity conditions, we derive non-asymptotic error bounds for the particle system approximation. In a few simple examples, including high dimensional product measures, bounds with explicit constants of feasible size are obtained. Our main motivation are applications to sequential MCMC methods for Monte Carlo integral estimation.
KeywordsMarkov chain Monte Carlo Sequential Monte Carlo Importance sampling Spectral gap Dirichlet forms Functional inequalities Feynman–Kac formula
Mathematics Subject Classification (2000)65C05 60J25 60B10 47H20 47D08
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