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A Comparison of Marginal Likelihood Computation Methods

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  • Charles S. Bos

    (Vrije Universiteit Amsterdam)

Abstract

In a Bayesian analysis, different models can be compared on the basis of theexpected or marginal likelihood they attain. Many methods have been devised to compute themarginal likelihood, but simplicity is not the strongest point of most methods. At the sametime, the precision of methods is often questionable.In this paper several methods are presented in a common framework. The explanation of thedifferences is followed by an application, in which the precision of the methods is testedon a simple regression model where a comparison with analytical results is possible.

Suggested Citation

  • Charles S. Bos, 2002. "A Comparison of Marginal Likelihood Computation Methods," Tinbergen Institute Discussion Papers 02-084/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20020084
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    File URL: https://papers.tinbergen.nl/02084.pdf
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    References listed on IDEAS

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    1. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
    2. Hoeting, Jennifer & Raftery, Adrian E. & Madigan, David, 1996. "A method for simultaneous variable selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 251-270, July.
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    Cited by:

    1. Charles Bos & Neil Shephard, 2006. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 219-244.
    2. Zhenyu Zhao & Thomas A. Severini, 2017. "Integrated likelihood computation methods," Computational Statistics, Springer, vol. 32(1), pages 281-313, March.
    3. Pierangelo De Pace, 2005. "Grid-Bootstrap Methods vs. Bayesian Analysis. Testing for Structural Breaks in the Conditional Variance of Nominal Interest Rate Spreads - Four Cases in Europe," Econometrics 0509011, University Library of Munich, Germany, revised 14 Feb 2006.
    4. Li, Gong & Shi, Jing, 2010. "Application of Bayesian model averaging in modeling long-term wind speed distributions," Renewable Energy, Elsevier, vol. 35(6), pages 1192-1202.
    5. Jochen Ranger & Jorg-Tobias Kuhn, 2012. "A flexible latent trait model for response times in tests," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 31-47, January.

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    More about this item

    Keywords

    Marginal likelihood; Bayesian analysis.;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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