  It is proposed in the literature that in some complicated problems maximum
likelihood estimates (MLE) are not suitable or even do not exist. An
alternative to MLE for estimation of the parameters is the Bayesian method. The
Markov chain Monte Carlo (MCMC) simulation procedure is designed to fit
Bayesian models. Bayesian method like classical method (MLE) has advantages and
disadvantages. One of the advantages of Bayesian method over MLE method is the
ability of saving the information included in past data through the posterior
distributions of the model parameters to be used for modelling future data. In
this article we investigate the performance of Bayesian method in modelling
dynamic binary data when the data are growing over time and individuals.
