Comparisons of Markov Chain Monte Carlo Convergence Diagnostic Tests for Bayesian Logistic Random Effect Models

In mixed models, posterior densities are too difficult to work with directly. With the Markov chain Monte Carlo (MCMC) methods, to do statistical inference requires the convergence of the MCMC chain to its stationary distribution. To assess convergence of Markov chain has not a specific way. Assessing convergence of Markov chain has been developed many techniques. Although increasingly popularity MCMC methods, use of MCMC convergence diagnostics is not still common. Usually, the most MCMC users address the convergence problem by applying a diagnostic means by running their samplers. That’s why in this article we discuss the problem of assessing the performance of MCMC algorithms. We compare convergence diagnostic tests to achieve target distribution. Bayesian logistic random effects models are intended to be interpreted with emphasis on mathematical expansions under these techniques. We have used a real data that wants to know what patient and physician factors are most related to whether a patient's lung cancer goes into remission after treatment outcomes and quality of life in patients with lung cancer. Our results show that convergence needs to enough time and experience.