The Alive Particle Filter and Its Use in Particle Markov Chain Monte Carlo

DOI10.1080/07362994.2015.1060892zbMath1337.82019OpenAlexW2195963939MaRDI QIDQ3459223

Pierre Del Moral, Christopher Yau, Xiaole Zhang, Ajay Jasra, Anthony J. T. Lee

Publication date: 21 December 2015

Published in: Stochastic Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07362994.2015.1060892

zbMATH Keywords

Markov chain Monte Carloparticle filtersFeynman-Kac formulae

Mathematics Subject Classification ID

Related Items (17)

Twisting the alive particle filter ⋮ Delayed Acceptance ABC-SMC ⋮ Unbiased simulation of rare events in continuous time ⋮ Accelerating inference for stochastic kinetic models ⋮ Adaptive multilevel splitting: Historical perspective and recent results ⋮ Efficient Bayesian model choice for partially observed processes: with application to an experimental transmission study of an infectious disease ⋮ Stochastic epidemic models inference and diagnosis with Poisson random measure data augmentation ⋮ Exact and approximate Bayesian inference for low integer-valued time series models with intractable likelihoods ⋮ Filtering and estimation for a class of stochastic volatility models with intractable likelihoods ⋮ Alive SMC²: Bayesian model selection for low‐count time series models with intractable likelihoods ⋮ Approximate Inference for Observation-Driven Time Series Models with Intractable Likelihoods ⋮ Bayesian model discrimination for partially-observed epidemic models ⋮ On the performance of particle filters with adaptive number of particles ⋮ Updating variational Bayes: fast sequential posterior inference ⋮ Efficient \(\mathrm{SMC}^2\) schemes for stochastic kinetic models ⋮ Importance sampling for partially observed temporal epidemic models ⋮ Efficient sampling of conditioned Markov jump processes

Cites Work

• The pseudo-marginal approach for efficient Monte Carlo computations
• Sequential Monte Carlo for rare event estimation
• An adaptive sequential Monte Carlo method for approximate Bayesian computation
• Filtering via approximate Bayesian computation
• A nonasymptotic theorem for unnormalized Feynman-Kac particle models
• Stochastic orders
• Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters
• Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
• On mixtures of \(\chi^ 2\)-and \(F\)-distributions which yield distributions of the same family
• Parameter Estimation for Hidden Markov Models with Intractable Likelihoods
• Particle Motions in Absorbing Medium with Hard and Soft Obstacles
• A variant of importance splitting for rare event estimation
• Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
• Approximate Bayesian Computation for Smoothing

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