COMPUTATIONAL ALGORITHMS IN BAYESIAN INFERENCES FOR A NORMAL-MEAN WITH T PRIOR DISTRIBUTIONS

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27815

Title:	COMPUTATIONAL ALGORITHMS IN BAYESIAN INFERENCES FOR A NORMAL-MEAN WITH T PRIOR DISTRIBUTIONS
Authors:	FAN,TH
Contributors:	統計研究所
Date:	1994
Issue Date:	2010-06-29 19:34:14 (UTC+8)
Publisher:	中央大學
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.
Relation:	COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	414	View/Open

社群 sharing