Objective Priors for Discrete Parameter Spaces
This article considers the development of objective prior distributions for discrete parameter spaces. Formal approaches to such development—such as the <italic>reference prior</italic> approach—often result in a constant prior for a discrete parameter, which is questionable for problems that exhibit certain types of structure. To take advantage of structure, this article proposes embedding the original problem in a continuous problem that preserves the structure, and then using standard reference prior theory to determine the appropriate objective prior. Four different possibilities for this embedding are explored, and applied to a population-size model, the hypergeometric distribution, the multivariate hypergeometric distribution, the binomial-beta distribution, and the binomial distribution. The recommended objective priors for the first, third, and fourth problems are new.
Year of publication: |
2012
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Authors: | Berger, James O. ; Bernardo, Jose M. ; Sun, Dongchu |
Published in: |
Journal of the American Statistical Association. - Taylor & Francis Journals, ISSN 0162-1459. - Vol. 107.2012, 498, p. 636-648
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Publisher: |
Taylor & Francis Journals |
Saved in:
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