Importance sampling for partially observed temporal epidemic models

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...

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Bibliographic Details
Published inStatistics and computing Vol. 29; no. 4; pp. 617 - 630
Main Author Black, Andrew J.
Format Journal Article
LanguageEnglish
Published New York Springer US 15.07.2019
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Bayesian analysis
Computing time
Epidemics
Importance sampling
Markov analysis
Markov chains
Mathematics and Statistics
Probability and Statistics in Computer Science
Sampling techniques
Statistical inference
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Markov chain
Epidemic modelling
Particle filter
Importance sampling
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Summary: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.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-018-9827-1