Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models

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ID: 305243
2001
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Ranked #129 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
Summary Markov chain Monte Carlo (MCMC) sampling is a numerically intensive simulation technique which has greatly improved the practicality of Bayesian inference and prediction. However, MCMC sampling is too slow to be of practical use in problems involving a large number of posterior (target) distributions, as in dynamic modelling and predictive model selection. Alternative simulation techniques for tracking moving target distributions, known as particle filters, which combine importance sampling, importance resampling and MCMC sampling, tend to suffer from a progressive degeneration as the target sequence evolves. We propose a new technique, based on these same simulation methodologies, which does not suffer from this progressive degeneration.
Reference Key
openalex_W2168634963 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Walter R. Gilks, Carlo Berzuini
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2001
DOI
10.1111/1467-9868.00280
URL
https://academic.oup.com/jrsssb/article-pdf/63/1/127/49590283/jrsssb_63_1_127.pdf
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