A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood
Lennart Hoogerheide and
Herman van Dijk
Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3398-3414
Strategic choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior distributions. A comparative analysis is presented of possible advantages and limitations of different simulation techniques; of possible choices of candidate distributions and choices of target or warped target distributions; and finally of numerical standard errors. The importance of a robust and flexible estimation strategy is demonstrated where the complete posterior distribution is explored. Given an appropriately yet quickly tuned adaptive candidate, straightforward importance sampling provides a computationally efficient estimator of the marginal likelihood (and a reliable and easily computed corresponding numerical standard error) in the cases investigated, which include a non-linear regression model and a mixture GARCH model. Warping the posterior density can lead to a further gain in efficiency, but it is more important that the posterior kernel be appropriately wrapped by the candidate distribution than that it is warped.
Keywords: Marginal likelihood; Bayes factor; Importance sampling; Bridge sampling; Adaptive mixture of Student-t distributions (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only.
Working Paper: A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3398-3414
Access Statistics for this article
Computational Statistics & Data Analysis is currently edited by S.P. Azen
More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Haili He ().