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Estimation of posterior density functions from a posterior sample

✍ Scribed by Man-Suk Oh

Book ID
104306812
Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
193 KB
Volume
29
Category
Article
ISSN
0167-9473

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✦ Synopsis

The joint posterior density function of parameters and marginal posterior density functions of subsets of parameters are key quantities in Bayesian inference. Even when the posterior densities are unknown, there are many cases where Markov Chain Monte Carlo methods can generate samples from the joint posterior distribution. This paper proposes a simple and e cient method of estimating the posterior density functions at various points simultaneously by using a posterior sample.

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## Abstract We provide an overview of the use of kernel smoothing to summarize the quantitative trait locus posterior distribution from a Markov chain Monte Carlo sample. More traditional distributional summary statistics based on the histogram depend both on the bin width and on the sideway shift