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Dear All:

Problem:
I am currently using the method of Lewis-Raftery to estimate marginal likelihoods based on a 
single posterior draw (posterior mode):
http://www.stat.washington.edu/raftery/Research/PDF/lewis1997.pdf
I see some variation in my estimates, and would like to find ways to
estimate marginal likelihoods using _all_ draws from the posterior to decrease variation.

Solution:
This is a work in progress. I am not sure but I hope this might be an appropriate time to update
with regards to my progress.

I have been recommended some excellent articles by Chib, specifically:
http://www.cs.ubc.ca/~arnaud/stat535/chib_markov.pdf
http://www.isds.duke.edu/~scs/Courses/Stat376/Papers/NormConstants/ChibMHJASA2001.pdf

I have been warned however the second method might require some tuning.

Additionally, I have found an excellent review of some methods here:
http://epub.wu-wien.ac.at/dyn/virlib/wp/eng/showentry?ID=epub-wu-01_597

I have used some of the methods in section "2.1 Direct Methods" and am having
good luck with the Harmonic Mean Estimator described as, for my problem, the
likelihood seems invertible.

I am computing log's in the case of Direct Estimation both with parameters from
priors and from the posterior (the latter for the Harmonic Mean Estimator), and
have written short documents summarizing the use of a "normalizing" factor in these
approaches to avoid having to take the logarithms of very small numbers:
http://people.hnl.bcm.edu/misha/bayes/modelselection/averageOfLikelihoods.tex
http://people.hnl.bcm.edu/misha/bayes/modelselection/averageOfLikelihoods.pdf
http://people.hnl.bcm.edu/misha/bayes/modelselection/averageOfLikelihoodsHME.tex
http://people.hnl.bcm.edu/misha/bayes/modelselection/averageOfLikelihoodsHME.pdf

Thank you so much for all your help.

I will update on further progress.

Misha
-- 
Misha Koshelev
MD/PhD Student

Human Neuroimaging Laboratory
One Baylor Plaza
S104
Baylor College of Medicine
Houston, TX 77030

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