In the study of diverging populations, a common goal is to disentangle the conflicting signals of prolonged genetic drift (elevating divergence) and gene exchange (removing it). An isolation-with-migration (IM) model is a common framework for statistical analyses of evolutionary divergence. In this talk, I will present a new Bayesian method for estimating the divergence time of two populations and migration rates under an IM model. The new method resolves statistical limitations of mixing difficulty in a Markov chain Monte Carlo simulation, and hence overcomes major roadblocks for analyses of genome-scale data. The method also provides for the calculation of the joint posterior density for all model parameters, thus resolving the problem of high false positive rates that arise for the likelihood ratio tests for migration rates using other existing Bayesian approaches. With the implementation of the new method in the computer program MIST, I demonstrate the method’s accuracy, scalability and other advantages using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes (P. t.) troglodytes and P. t. verus.