On Divergence Measures Leading to Jeffreys and Other Reference Priors

被引：9

作者：

Liu, Ruitao ^{[1
,2
]}

Chakrabarti, Arijit

Samanta, Tapas

Ghosh, Jayanta. K. ^{[3
]}

Ghosh, Malay ^{[4
]}

机构：

[1] Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA

[2] ACT Inc, Iowa City, IA USA

[3] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA

[4] Univ Florida, Dept Stat, Gainesville, FL 32611 USA

来源：

BAYESIAN ANALYSIS | 2014年 / 9卷 / 02期

关键词：

alpha-divergences; Jeffreys prior; Reference prior; EXPONENTIAL-FAMILIES; INFORMATION; DISTRIBUTIONS; ASYMPTOTICS; PARAMETER;

D O I：

10.1214/14-BA862

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The paper presents new measures of divergence between prior and posterior which are maximized by the Jeffreys prior. We provide two methods for proving this, one of which provides an easy to verify sufficient condition. We use such divergences to measure information in a prior and also obtain new objective priors outside the class of Bernardo's reference priors.

引用

页码：331 / 369

页数：39

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