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On a family of prior distributions for a class of Bayesian search models

Part of: Decision theory Stochastic processes

Published online by Cambridge University Press:  01 July 2016

K. D. Glazebrook
K. D. Glazebrook*
Affiliation:
University of Newcastle upon Tyne
*
∗ Postal address: Department of Mathematics and Statistics, The University, Newcastle upon Tyne NE1 7RU, UK.
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Abstract

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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.

Keywords

BAYES SEQUENTIAL DECISION RULEGITTINS INDEXOPTIMAL STOPPINGSEARCH MODELS

MSC classification

Primary: 62C10: Bayesian problems; characterization of Bayes procedures
Secondary: 60G40: Stopping times; optimal stopping problems; gambling theory
Type
Letter to the Editor
Information
Advances in Applied Probability , Volume 25 , Issue 3 , September 1993 , pp. 714 - 716
Copyright
Copyright © Applied Probability Trust 1993 

References

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