Abstract:
Posterior distribution in Bayesian statistical inference provides a summary of knowledge about the ϴ parameter from a given sample data. Posterior distribution is a product of the likelihood times prior. Beta distribution is considered to be a suitable conjugate prior of binomial distribution. Beta I distribution is widely applied as prior but not Beta II distribution. This paper therefore derives posterior distribution using Beta II as prior distribution in Bayesian inference. The distribution properties such as jth moment, mean and variance are verified and the results obtained discussed
