Bayesian Inference For The Beta-Binomial Distribution Via Polynomial Expansions
Journal Of Computational And Graphical Statistics
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 Beta-Binomial. 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.
Philip J. Everson and E. T. Bradlow.
"Bayesian Inference For The Beta-Binomial Distribution Via Polynomial Expansions".
Journal Of Computational And Graphical Statistics.
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