Bayesian Inference for Binomial Models with Conjugate Priors Based on Generalizations of the Beta Distribution
Bayesian; Beta Distribution; Binomial Model; Posterior; Prior.
This 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 the Bayesian approach, employing generalizations of the beta distribution as priors, so 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 generated from random numbers to assess their advantages over the beta distribution. In terms of fit, the goal is to achieve better results than those obtained by the beta distribution in comparative analyses.