Abstract This paper outlines a framework for quantifying the prior’s contribution to posterior inference in presence of prior-likelihood discordance, broader concept than usual notion conflict. We achieve this dual purpose by extending classic prior sample size, M, three directions: (I) estimating M beyond conjugate families; (II) formulating as relative that is function likelihood size k, M(k)...

A default prior distribution is proposed for the Bayesian analysis of contingency tables. The prior is specified to allow for dependence between levels of the factors. Different dependence structures are considered, including conditional autoregressive and distance correlation structures. To demonstrate the prior distribution, a dataset is considered which involves estimating the number of inje...

In this paper, it has been dealt with basic Gompertz distribution. The maximum likelihood, Bayes methods of estimation were used to estimate the unknown shape parameter. failure rate (hazard) function least loss was found using different priors (Gamma, exponential, chi-square and triple prior) under symmetric (Degroot function). A comparison made about performance these estimators numerical sol...

Bayesian approach remained rather unsuccessful in treating nonparametric problems. This is primarily due to the difficulty in finding workable prior distribution on the parameter space , which in nonparametric problems is taken to be a set of probability distributions on a given sample space. Two Desirable Properties of a Prior 1. The support of the prior should be large. 2. Posterior distribut...

We place a standard Dirichlet process prior on the inverse-link function of a binary regression model and study the posterior distribution as the prior precision parameter converges to zero. Simple closed-form expressions are available for the limiting posterior density of the regression parameters and the posterior predictive distribution. This limiting posterior exhibits instability as the sa...