Postauditing and Cost Estimation Applications: An Illustration of MCMC Simulation for Bayesian Regression Analysis

Abstract

Abstract Often in Bayesian anlysis closed-form posteriors cannot be derived for complex models. However, it is important to be able to do Bayesian analysis relatively easily. This article presents an alternative, the more general Markov chain Monte Carlo (MCMC) simulation approach, which permits the efficient development of posterior distributions. MCMC simulation methods are now becoming the state of the art in numerous empirical and analytical applications in applied mathematics, biostatistics, marketing, economics, and other areas, but those methods are noticeably absent in the engineering economic analysis literature. The purpose of this article is to introduce MCMC simulation methods to the engineering economics research and practitioner community. Using postaudits and cost estimation as application areas, the article focuses on what MCMC simulation entails, its advantages, and its disadvantages and highlights the usefulness and versatility of the approach.