Computational algorithms in bayesian inferences for a normal mean with t prior distributions

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Abstract

Bayesian inference is considered when both the likelihood and the prior distributions are t-densities. Some efficient calculational algorithms in basic normal inference problems concerning the mean over a range of the prior parameters are compared. The algorithms discussed include an approximation via Taylor expansion, the Naylor-Smith algorithm, and the exact formulas developed earlier. Each of them has some drawbacks in terms of accuracy or speed. A combination for efficient calculation over a grid of the prior parameters is suggested.

Original languageEnglish
Pages (from-to)1155-1174
Number of pages20
JournalCommunications in Statistics - Simulation and Computation
Volume23
Issue number4
DOIs
StatePublished - 1 Jan 1994

Keywords

  • likelihood
  • posterior mean
  • posterior variance
  • prior distribution

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