Estimation of posterior density functions from a posterior sample

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21 Scopus citations


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 efficient method of estimating the posterior density functions at various points simultaneously by using a posterior sample.

Original languageEnglish
Pages (from-to)411-427
Number of pages17
JournalComputational Statistics and Data Analysis
Issue number4
StatePublished - 28 Feb 1999


  • Bayesian analysis
  • Density estimation
  • Markov Chain Monte Carlo
  • Simulation


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