The paper entitled “Automatically Finding the Right Probabilities in Bayesian Networks” by Bahare Salmani and Joost-Pieter Katoen has been accepted for publication in the Journal of Artificial Intelligence Research. The paper presents a way to use probabilistic model checking to perform inference on Bayesian networks (BNs) and find suitable parameter values in the case of parametric BNs. The approach improves upon the state of the art by—most importantly—being able to handle an unbounded number of parameters anywhere in the conditional probability tables of the BN.