Accompanying this have been new approaches to data analysis using, for example, Markov Chain Monte Carlo simulations that are hugely computer intensive.

Given the observed hydrological data, the model can estimate the posterior probability distribution of each location of change-point by using the Monte Carlo Markov Chain (MCMC) sampling method.

In this paper I suppose an MCMC random model generating procedure that can generate a model with the lowest criterion value.