In computational statistics, reversible-jump Markov chain Monte Carlo is an extension to standard Markov chain Monte Carlo (MCMC) methodology that allows simulation of the posterior distribution on spaces of varying dimensions.Thus, the simulation is possible even if the number of parameters in the model is not known. Let be a model indicator and the parameter space whose number of dimensions depends on the model . The model indication need not be finite. The stationary distribution is the joint posterior distribution of that takes the values . The function with