This chapter describes application of Bayesian methodology to spatial econometric modeling and estimation. Bayesian methodology has existed for a long time, but recent approaches to estimation of these models have led to a revival of interest in these methods. The estimation approach known as Markov Chain Monte Carlo (MCMC) decomposes complicated estimation problems into simpler problems that rely on the conditional distributions for each parameter in the model (Gelfand and Smith, 1990). This innovation makes application of the Bayesian methodology far easier than past approaches that relied on analytical solution of the posterior distribution. A result of this is that an extensible toolkit for solving spatial econometric estimation problems can be developed at both a theoretical and applied level.