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

研究成果: 雜誌貢獻期刊論文同行評審

摘要

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.

原文???core.languages.en_GB???
頁(從 - 到)1155-1174
頁數20
期刊Communications in Statistics - Simulation and Computation
23
發行號4
DOIs
出版狀態已出版 - 1 1月 1994

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