Augmentation schemes for particle MCMC

DOI10.1007/s11222-015-9603-4zbMath1356.65025arXiv1408.6980OpenAlexW3121363706MaRDI QIDQ341152

Loukia Meligkotsidou, Paul Fearnhead

Publication date: 16 November 2016

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

Full work available at URL: https://arxiv.org/abs/1408.6980

zbMATH Keywords

stochastic volatilitystate-space modelssequential Monte CarloDirichlet process mixture modelsparticle Gibbs

Mathematics Subject Classification ID

Non-Markovian processes: estimation (62M09) Probabilistic models, generic numerical methods in probability and statistics (65C20) Monte Carlo methods (65C05) Stochastic particle methods (65C35)

Related Items (7)

Efficient data augmentation techniques for some classes of state space models ⋮ A point mass proposal method for Bayesian state-space model fitting ⋮ A flexible particle Markov chain Monte Carlo method ⋮ Conditional particle filters with diffuse initial distributions ⋮ Full Bayesian inference in hidden Markov models of plant growth ⋮ Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes ⋮ On particle methods for parameter estimation in state-space models

Uses Software

• STRUCTURE
• Eigenstrat

Cites Work

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