Importance sampling for partially observed temporal epidemic models

DOI10.1007/s11222-018-9827-1zbMath1430.62030arXiv1801.08244OpenAlexW3106082972WikidataQ58841092 ScholiaQ58841092MaRDI QIDQ2329788

Andrew J. Black

Publication date: 18 October 2019

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1801.08244

zbMATH Keywords

importance samplingMarkov chainBayesian inferenceparticle filterepidemic modelling

Mathematics Subject Classification ID

Epidemiology (92D30) Bayesian inference (62F15) Sampling theory, sample surveys (62D05) Markov processes: hypothesis testing (62M02)

Related Items (3)

Direct statistical inference for finite Markov jump processes via the matrix exponential ⋮ Bayesian model discrimination for partially-observed epidemic models ⋮ EpiStruct

Uses Software

• GitHub

Cites Work

• On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
• Simulation-based Bayesian inference for epidemic models
• Efficient \(\mathrm{SMC}^2\) schemes for stochastic kinetic models
• On the efficiency of pseudo-marginal random walk Metropolis algorithms
• Computation of epidemic final size distributions
• Sequential Monte Carlo Methods in Practice
• Handbook of Monte Carlo Methods
• The Alive Particle Filter and Its Use in Particle Markov Chain Monte Carlo
• Particle Markov Chain Monte Carlo Methods
• Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
• Alive SMC²: Bayesian model selection for low‐count time series models with intractable likelihoods
• Bayesian analysis for emerging infectious diseases
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