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Type: Journal article
Title: Importance sampling for partially observed temporal epidemic models
Author: Black, A.
Citation: Statistics and Computing, 2019; 29(4):617-630
Publisher: Springer
Issue Date: 2019
ISSN: 0960-3174
Statement of
Andrew J. Black
Abstract: We present an importance sampling algorithm that can produce realisations of Markovian epidemic models that exactly match observations, taken to be the number of a single event type over a period of time. The importance sampling can be used to construct an efficient particle filter that targets the states of a system and hence estimate the likelihood to perform Bayesian inference. When used in a particle marginal Metropolis Hastings scheme, the importance sampling provides a large speed-up in terms of the effective sample size per unit of computational time, compared to simple bootstrap sampling. The algorithm is general, with minimal restrictions, and we show how it can be applied to any continuous-time Markov chain where we wish to exactly match the number of a single event type over a period of time.
Keywords: Importance sampling; Markov chain; epidemic modelling; particle filter
Rights: © Springer Science+Business Media, LLC, part of Springer Nature 2018
DOI: 10.1007/s11222-018-9827-1
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