Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method

DOI10.1214/17-BA1077zbMath1448.65013arXiv1602.06030OpenAlexW2964275218MaRDI QIDQ1631578

Alexander Y. Shestopaloff, Radford M. Neal

Publication date: 6 December 2018

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.06030

zbMATH Keywords

MCMCnonlinear state space

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (3)

A point mass proposal method for Bayesian state-space model fitting ⋮ Conditional sequential Monte Carlo in high dimensions ⋮ Limit theorems for sequential MCMC methods

Cites Work

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• Sequential Monte Carlo Methods in Practice
• Particle Markov Chain Monte Carlo for Efficient Numerical Simulation
• Filtering via Simulation: Auxiliary Particle Filters
• Particle Markov Chain Monte Carlo Methods

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