Moala, Fernando A. [UNESP]Garcia, Lívia M. [UNESP]2014-05-272014-05-272013-07-01Quality Engineering, v. 25, n. 3, p. 282-291, 2013.0898-21121532-4222http://hdl.handle.net/11449/75788The exponential-logarithmic is a new lifetime distribution with decreasing failure rate and interesting applications in the biological and engineering sciences. Thus, a Bayesian analysis of the parameters would be desirable. Bayesian estimation requires the selection of prior distributions for all parameters of the model. In this case, researchers usually seek to choose a prior that has little information on the parameters, allowing the data to be very informative relative to the prior information. Assuming some noninformative prior distributions, we present a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Jeffreys prior is derived for the parameters of exponential-logarithmic distribution and compared with other common priors such as beta, gamma, and uniform distributions. In this article, we show through a simulation study that the maximum likelihood estimate may not exist except under restrictive conditions. In addition, the posterior density is sometimes bimodal when an improper prior density is used. © 2013 Copyright Taylor and Francis Group, LLC.282-291engBayesianexponential-logarithmic distributionJeffreysMCMCnoninformative priorposteriorNon-informative priorMaximum likelihood estimationBayesian networksArtigo10.1080/08982112.2013.764431WOS:000320223400008Acesso restrito2-s2.0-8487912146916212695523666970000-0002-2445-0407