This article deals with substantial computation component and discusses powerful computers and simulation algorithms that lead to model development where Bayesian econometric methods are predominant. It includes flexible or nonparametric models. It also distinguishes econometrics from statistics by its combination of economic theory with statistics. This article addresses principles, methods, and applications in different parts. It deals with a set of issues namely, the use of computationally intensive posterior simulation algorithms, heterogeneity, and problems caused by proliferation of parameters. The models discussed in this article have increased range and level of complication. They are strongly infused with economic theory and decision-theoretic issues. This article covers a broad range of the methods and models used by Bayesian econometricians in a wide variety of fields.