Studies on social animals often seek to identify dominance hierarchies, in which individuals are ranked according to competitive abilities based on counts of wins and losses in pairwise encounters. I illustrate Bayesian approaches, based on the method of paired comparisons, for determining ranks and for estimating relationships between dominance ability and other attributes. Bayesian inference combines prior probability distributions for each unknown parameter with likelihood functions to produce the joint posterior probability distribution for the quantities of interest. In contrast to nonparametric techniques for inferring ranks, Bayesian models yield measures of certainty for each inference and allow rigorous estimates of correlations between ranks and covariates even when there is considerable uncertainty as to the ranks themselves. A possible objection to the Bayesian approach is that it appears to entail more restrictive assumptions than do simpler methods. However, simulations show that Bayesian inferences are more robust to deviations from these assumptions than are the results of nonparametric methods.