Accept–reject Metropolis–Hastings sampling and marginal likelihood estimation
Siddhartha Chib and
Ivan Jeliazkov ()
Statistica Neerlandica, 2005, vol. 59, issue 1, 30-44
We describe a method for estimating the marginal likelihood, based on Chib (1995) and Chib and Jeliazkov (2001), when simulation from the posterior distribution of the model parameters is by the accept–reject Metropolis–Hastings (ARMH) algorithm. The method is developed for one‐block and multiple‐block ARMH algorithms and does not require the (typically) unknown normalizing constant of the proposal density. The problem of calculating the numerical standard error of the estimates is also considered and a procedure based on batch means is developed. Two examples, dealing with a multinomial logit model and a Gaussian regression model with non‐conjugate priors, are provided to illustrate the efficiency and applicability of the method.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15) Track citations by RSS feed
Downloads: (external link)
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:bla:stanee:v:59:y:2005:i:1:p:30-44
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0039-0402
Access Statistics for this article
Statistica Neerlandica is currently edited by P. H. Franses
More articles in Statistica Neerlandica from Netherlands Society for Statistics and Operations Research
Bibliographic data for series maintained by Wiley Content Delivery ().