Abstract
It is argued that the posterior predictive distribution for the binomial and multinomial distributions, when viewed via a hypergeometric-like representation, suggests the uniform prior on the parameters for these models. The argument is supported by studying variations on an example by Fisher, and complements Bayes' original argument for a uniform prior predictive distribution for the binomial. The fact that both arguments lead to invariance under transformation is also discussed.
Citation
Richard Gerlach. Kerrie Mengersen. Frank Tuyl. "Posterior predictive arguments in favor of the Bayes-Laplace prior as the consensus prior for binomial and multinomial parameters." Bayesian Anal. 4 (1) 151 - 158, March 2009. https://doi.org/10.1214/09-BA405
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