On adaptive resampling strategies for sequential Monte Carlo methods
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In this paper, the convergence analysis of a class of sequential Monte Carlo (SMC) methods where the times at which resampling occurs are computed online using criteria such as the effective sample size is studied.Abstract:
Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the convergence analysis of a class of SMC methods where the times at which resampling occurs are computed online using criteria such as the effective sample size. This is a popular approach amongst practitioners but there are very few convergence results available for these methods. By combining semigroup techniques with an original coupling argument, we obtain functional central limit theorems and uniform exponential concentration estimates for these algorithms.read more
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References
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BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Book
Monte Carlo strategies in scientific computing
TL;DR: This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared.
Journal ArticleDOI
Sequential Monte Carlo samplers
TL;DR: In this paper, the authors propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant.
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Inference in Hidden Markov Models
TL;DR: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory, and builds on recent developments to present a self-contained view.
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Feynman-Kac formulae : genealogical and interacting particle systems with applications
TL;DR: In this paper, the origins of Feynman-Kac and Particle Models are discussed and an overview of the evolution and evolution of these models is given, as well as a discussion of some of the properties of the models.