Bayesian model selection shows extremely polarized behavior when the models are wrong

Scientists from University College London (UCL) and the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS, AMSS), have reported progress in understanding problems associated with Bayesian model selection. The research suggests that the Bayesian method tends to produce very high-posterior probabilities for estimated evolutionary trees even if the trees are clearly wrong, and offers a possible explanation for this phenomenon.