Global sampling for sequential filtering over discrete state space
type: Item
instance of: scholarly article
title: Global sampling for sequential filtering over discrete state space
zbMATH Open document ID: 1075.62085
DOI: 10.1155/S1110865704407173
author: Pascal Cheung-Mon-Chan
author: Eric Moulines
published in: EURASIP Journal on Applied Signal Processing
publication date: 2005-05-03T00:00:00Z
review text: Summary: In many situations, there is a need to approximate a sequence of probability measures over a growing product of finite spaces. Whereas it is in general possible to determine analytic expressions for these probability measures, the number of computations needed to evaluate these quantities grows exponentially thus precluding real-time implementation. Sequential Monte Carlo techniques (SMC), which consist in approximating the flow of probability measures by the empirical distribution of a finite set of particles, are attractive techniques for addressing this type of problems. We present a simple implementation of the sequential importance sampling/resampling (SISR) technique for approximating these distributions; this method relies on the fact that, the space being finite, it is possible to consider every offspring of the trajectory of particles. The procedure is straightforward to implement, and well-suited for practical implementation. A limited Monte Carlo experiment is carried out to support our findings.
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 62L99
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 2163856
zbMATH Keywords: global sampling algorithm
zbMATH Keywords: fixed lag smoothing
zbMATH Keywords: particle filters
zbMATH Keywords: conditionally linear Gaussian state space models
zbMATH Keywords: autoregressive models
MaRDI profile type: Publication
full work available at URL: s1110865704407173
OpenAlex ID: W2136307643
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description: scientific article; zbMATH DE number 2163856