Bayesian Inference for the Exponential Power Function Parameters

Abstract

This paper addresses the problem of obtaining the marginal posterior distributions, via Gibbs Sampler, for the parameters of the well-known generalized error distribution called Exponential Power Function (E.P.F.). This density represents a family of unimodal symmetric distributions with shapes varying from leptokurtic to platikurtic.