Nonparametric prior elicitation for a binomial proportion

This paper proposes a nonparametric Bayesian approach based on a density estimation with an open unit interval (0,1) using binomial data. We propose a very efficient nonparametric Bayesian approach method to infer smooth density defined on (0,1) through the transformation of a random variable. For practical implementation, we provide the corresponding blocked Gibbs sampling procedure based on the stick-breaking representation. The greatest advantage of this method is that it does not require us to draw from the complete conditional posterior distribution using a Metropolis-Hastings transition probability because the proposed transformation leads to a pair of conjugate priors and likelihoods. The validity of the proposed method is assessed through simulated and real data analysis.

Automated summary

This paper proposes a nonparametric Bayesian approach for density estimation on an open unit interval (0,1) using binomial data. The proposed method efficiently infers the smooth density defined on (0,1) through the transformation of a random variable. A blocked Gibbs sampling procedure based on the stick-breaking representation is provided for practical implementation, which avoids the use of Metropolis-Hastings transition probability.