The social relations model (SRM) is a conceptual, methodological, and analytical approach that is widely used to examine dyadic behaviors and interpersonal perception within groups. This article introduces a general and flexible approach to estimating the parameters of the SRM that is based on Bayesian methods using Markov chain Monte Carlo techniques. The Bayesian approach overcomes several statistical problems that have plagued SRM researchers. First, it provides a single unified approach to estimating SRM parameters that can be easily extended to more specialized models (e.g., measurement models, moderator variables, categorical outcome variables). Second, sampling-based Bayesian methods allow statistically reliable inferences to be made about variance components and correlations, even with small sample sizes. Third, the Bayesian approach is able to handle designs with missing data. In a simulation study, the statistical properties (bias, root-mean-square error, coverage rate) of the parameter estimates produced by the Bayesian approach are compared with those of the method of moment estimates that have been used in previous research. A data example is presented to illustrate how discrete person moderators can be included in SRM analyses using the Bayesian approach. Finally, further extensions of the SRM are discussed, and suggestions for applied research are made.