On the use of marginal posteriors in marginal likelihood estimation via importance sampling

DOI10.1016/j.csda.2014.03.004zbMath1506.62147arXiv1311.0674OpenAlexW2035213284MaRDI QIDQ1623576

Konstantinos Perrakis, Efthymios G. Tsionas, Ioannis Ntzoufras

Publication date: 23 November 2018

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

Full work available at URL: https://arxiv.org/abs/1311.0674

zbMATH Keywords

importance samplingRao-Blackwellizationrandom effect modelsfinite normal mixturesmarginal likelihood estimationmarginal posterior

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Point estimation (62F10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

Thermodynamic Bayesian model comparison, Computing marginal likelihoods via the Fourier integral theorem and pointwise estimation of posterior densities, Bounded rationality and thick frontiers in stochastic frontier analysis, Bayes factor covariance testing in item response models, Note on posterior inference for the Bingham distribution, Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions, Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity, Scalable Bayesian Regression in High Dimensions With Multiple Data Sources, Bayesian analysis of static and dynamic Hurst parameters under stochastic volatility, Modeling dependence structures for response times in a Bayesian framework

Uses Software

• WinBUGS
• MultiNest
• tsbridge
• bayesmix
• PRMLT

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