Bayesian Inference for Binomial Models with Conjugate Priors Based on Generalizations
of the Beta Distribution
Bayesian Inference; Beta distribution; Binomial Model; Prior; Posterior
The present dissertation aims to propose alternative conjugate prior distributions for
the binomial model based on generalizations of the beta distribution. In this context, the
developed methodology seeks to estimate the proportion parameter π of the binomial
distribution using a Bayesian approach, employing generalizations of the beta distribution as
priors in such a way that the posterior distribution also belongs to the same class as the prior.
Additionally, the properties of these distributions will be studied in detail, with simulations
conducted through random number generation. Their advantages over the beta distribution
will be assessed, as the aim is to achieve better fitting results than those obtained by the beta
distribution when compared;