The Gibbs sampler with particle efficient importance sampling for state-space models*

DOI10.1080/07474938.2018.1536098zbMath1490.62080arXiv1601.01125OpenAlexW3125486059MaRDI QIDQ5860963

Roman Liesenfeld, Oliver Grothe, Tore Selland Kleppe

Publication date: 4 March 2022

Published in: Econometric Reviews (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1601.01125

zbMATH Keywords

Markov chain Monte Carlosequential importance samplingefficient importance samplingancestor samplingdynamic latent variable models

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (4)

Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates ⋮ Importance Sampling-Based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models ⋮ Modeling realized covariance measures with heterogeneous liquidity: a generalized matrix-variate Wishart state-space model ⋮ Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets

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