Error Bounds and Normalising Constants for Sequential Monte Carlo Samplers in High Dimensions
نویسندگان
چکیده
منابع مشابه
Error Bounds and Normalizing Constants for Sequential Monte Carlo Samplers in High Dimensions
In this article we develop a collection of results associated to the analysis of the Sequential Monte Carlo (SMC) samplers algorithm, in the context of high-dimensional i.i.d. target probabilities. The SMC samplers algorithm can be designed to sample from a single probability distribution, using Monte Carlo to approximate expectations w.r.t. this law. Given a target density in d−dimensions our ...
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In a recent paper [3], the Sequential Monte Carlo (SMC) sampler introduced in [12, 19, 24] has been shown to be asymptotically stable in the dimension of the state space d at a cost that is only polynomial in d, when N the number of Monte Carlo samples, is fixed. More precisely, it has been established that the effective sample size (ESS) of the ensuing (approximate) sample and the Monte Carlo ...
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In this paper, we provide bounds on the asymptotic variance for a class of sequential Monte Carlo (SMC) samplers designed for approximating multimodal distributions. Such methods combine standard SMC methods and Markov chain Monte Carlo (MCMC) kernels. Our bounds improve upon previous results, and unlike some earlier work, they also apply in the case when the MCMC kernels can move between the m...
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In this paper, we propose a methodology to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time using Sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make para...
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This paper addresses nite sample stability properties of sequential Monte Carlo methods for approximating sequences of probability distributions. The results presented herein are applicable in the scenario where the start and end distributions in the sequence are xed and the number of intermediate steps is a parameter of the algorithm. Under assumptions which hold on non-compact spaces, it is s...
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ژورنال
عنوان ژورنال: Advances in Applied Probability
سال: 2014
ISSN: 0001-8678,1475-6064
DOI: 10.1239/aap/1396360114