ON A FAMILY OF PRIOR DISTRIBUTIONS FOR A CLASS OF BAYESIAN SEARCH MODELS

被引：1
|
作者：
GLAZEBROOK, KD
机构：
来源：
ADVANCES IN APPLIED PROBABILITY | 1993年 / 25卷 / 03期
关键词：
BAYES SEQUENTIAL DECISION RULE; GITTINS INDEX; OPTIMAL STOPPING; SEARCH MODELS;
D O I：
10.2307/1427532
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We propose a two-parameter family of conjugate prior distributions for the number of undiscovered objects in a class of Bayesian search models. The family contains the one-parameter Euler and Heine families as special cases. The two parameters may be interpreted respectively as an overall success rate and a rate of depletion of the source of objects. The new family gives enhanced flexibility in modelling.
引用
收藏
页码：714 / 716
页数：3
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