MCMC for Markov-switching models—Gibbs sampling vs. marginalized likelihood

DOI10.1080/03610918.2019.1565580zbMath1489.62286OpenAlexW2911832412MaRDI QIDQ5082562

Tore Selland Kleppe, Kjartan Kloster Osmundsen, Atle Oglend

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1565580

zbMATH Keywords

marginalized likelihoodMarkov-switchingHamiltonian Monte Carlo

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Uses Software

• R
• bayesm
• JAGS
• Rcpp
• Stan
• Label.switching
• DRAGON
• NUTS

Cites Work

• Unnamed Item
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• Bayesian Methods for Hidden Markov Models
• Dealing With Label Switching in Mixture Models
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Particle Markov Chain Monte Carlo Methods
• Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
• Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

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