Posterior predictive arguments in favor of the Bayes-Laplace prior as the consensus prior for binomial and multinomial parameters
By Frank Tuyl, Richard Gerlach, Kerrie L. Mengersen. 2009
50 citations recorded by the source corpus.
It is argued that the posterior predictive distribution for the binomial and multinomial distributions, when viewed via a hypergeometric-like representa- tion, 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 bino- mial. The fact that both arguments lead to invariance under transformation is also discussed.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/af3c1b477f54fa7b93ce047235fe1e2fff9e736a
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In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- Posterior propriety in Bayesian extreme value analyses using reference priors (2015, background): “eshold excesses is combined with a binomial(N;p u) model for the number of excesses, where p u= P(X>u). Reference priors for a binomial probability have been studied extensively, see, for example, Tuyl et al. (2009). An approximately equivalent approach is the non-homogeneous Poisson process (NHP”