The marginal likelihood of dynamic mixture models

DOI10.1016/j.csda.2012.03.007zbMath1255.62062OpenAlexW2022236795MaRDI QIDQ1927041

Christophe Planas, Gabriele Fiorentini, Alessandro Rossi

Publication date: 30 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.03.007

zbMATH Keywords

bridge samplingLaplace methodstate space modelsBayesian model selectionMarkov switching modelsChib methodreciprocal importance sampling

Mathematics Subject Classification ID

Related Items (1)

Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models

Uses Software

• tsbridge

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A unified approach to nonlinearity, structural change, and outliers
• Bayesian forecasting and dynamic models.
• Solving linear rational expectations models: A horse race
• Calculating posterior distributions and modal estimates in Markov mixture models
• The diffuse Kalman filter
• Exact mean integrated squared error
• Solutions to linear rational expectations models: a compact exposition
• Bayes inference in regression models with ARMA\((p,q)\) errors
• A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood
• On marginal likelihood computation in change-point models
• Finite mixture and Markov switching models.
• Tailored randomized block MCMC methods with application to DSGE models
• Marginal Likelihood from the Gibbs Output
• Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques*
• Accurate Approximations for Posterior Moments and Marginal Densities
• A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
• Using simulation methods for bayesian econometric models: inference, development,and communication
• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
• On Gibbs sampling for state space models
• Estimating Bayes Factors via Posterior Simulation With the Laplace-Metropolis Estimator
• Bayesian Methods for Hidden Markov Models
• Efficient Bayesian Inference for Dynamic Mixture Models
• Marginal Likelihood From the Metropolis–Hastings Output
• Bayes Factors
• Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
• Bayesian Analysis of DSGE Models
• Three Multidimensional-integral Identities with Bayesian Applications
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

This page was built for publication: The marginal likelihood of dynamic mixture models