Markov chain Monte Carlo methods (MCMC) are iterative algorithms that are used in many Bayesian simulation studies, where the inference cannot be easily obtained directly through the defined model. Reversible jump MCMC methods belong to a special type of MCMC methods, in which the dimension of parameters can change in each iteration. In this study, we suggest Gibbs sampling in place of RJMCMC, to decrease the computational demand of the calculation of high dimensional systems. We evaluate the performance of the suggested algorithm in three real benchmark datasets, by comparing the accuracy and the computational demand with its strong alternatives, namely, birth-death MCMC, RJMCMC and QUIC algorithms. From the comparative analyses, we detect that Gibbs sampling improves the computational cost of RJMCMC without losing the accuracy.