We introduce a full-likelihood Bayesian approach, ecoevolity, which takes full advantage of information in genomic data. 
ecoevolity
Approximate-likelihood Bayesian computation (ABC) approaches to inferring such patterns from comparative genetic data are very sensitive to prior assumptions and often biased toward estimating shared divergences. 
Approximate-likelihood Bayesian computation (ABC)
By analytically integrating over gene trees, we are able to directly calculate the likelihood of the population history from genomic data, and efficiently sample the model-averaged posterior via Markov chain Monte Carlo algorithms. 
Markov chain Monte Carlo algorithms