Bayesian Inference for the Beta-Binomial Distribution via Polynomial Expansions

Citation data:

Journal of Computational and Graphical Statistics, ISSN: 1061-8600, Vol: 11, Issue: 1, Page: 202-207

Publication Year:
2002
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Abstract Views 80
Captures 14
Readers 14
Citations 11
Citation Indexes 11
Repository URL:
https://works.swarthmore.edu/fac-math-stat/10
DOI:
10.1198/106186002317375686
Author(s):
Philip J. Everson; Eric T. Bradlow
Publisher(s):
Informa UK Limited
Tags:
Mathematics; Decision Sciences; Statistics and Probability
article description
A commonly used paradigm in modeling count data is to assume that individual counts are generated from a Binomial distribution, with probabilities varying between individuals according to a Beta distribution. The marginal distribution of the counts is then BetaBinomial. Bradlow, Hardie, and Fader (2002, p. 189) make use of polynomial expansions to simplify Bayesian computations with Negative-Binomial distributed data. This article exploits similar expansions to facilitate Bayesian inference with data from the Beta-Binomial model. This has great application and computational importance to many problems, as previous research has resorted to computationally intensive numerical integration or Markov chain Monte Carlo techniques.