Approximate Bayesian Computation for Smoothing

DOI10.1080/07362994.2013.879262zbMath1429.62368arXiv1206.5208OpenAlexW1995432011WikidataQ56503237 ScholiaQ56503237MaRDI QIDQ5420646

Sumeetpal S. Singh, Emma J. McCoy, James S. Martin, Nick Whiteley, Ajay Jasra, Pierre Del Moral

Publication date: 13 June 2014

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

Full work available at URL: https://arxiv.org/abs/1206.5208

zbMATH Keywords

smoothinghidden Markov modelssequential Monte Carloapproximate Bayesian computation

Mathematics Subject Classification ID

Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Sequential estimation (62L12)

Related Items (14)

Twisting the alive particle filter ⋮ The Alive Particle Filter and Its Use in Particle Markov Chain Monte Carlo ⋮ A pseudo-marginal sequential Monte Carlo online smoothing algorithm ⋮ Approximate Bayesian Computation for a Class of Time Series Models ⋮ Approximate Bayesian Computation: A Survey on Recent Results ⋮ Likelihood-free approximate Gibbs sampling ⋮ On predictive inference for intractable models via approximate Bayesian computation ⋮ Backward Importance Sampling for Online Estimation of State Space Models ⋮ Likelihood-free inference in state-space models with unknown dynamics ⋮ Multilevel Monte Carlo in approximate Bayesian computation ⋮ Marginalized approximate filtering of state‐space models ⋮ Filtering and estimation for a class of stochastic volatility models with intractable likelihoods ⋮ Gradient free parameter estimation for hidden Markov models with intractable likelihoods ⋮ Adaptive kernels in approximate filtering of state‐space models

Cites Work

• Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
• Sequential Monte Carlo smoothing for general state space hidden Markov models
• Approximate Bayesian computational methods
• Filtering via approximate Bayesian computation
• Nonparametric particle filtering approaches for identification and inference in nonlinear state-space dynamic systems
• Smoothing algorithms for state-space models
• Inference in hidden Markov models.
• Mean Field Simulation for Monte Carlo Integration
• A backward particle interpretation of Feynman-Kac formulae
• Monte Carlo Smoothing for Nonlinear Time Series
• Monte Carlo strategies in scientific computing

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