Bayesian analysis and decisions in nuclear power plant maintenance

It is somewhat true that in most mainstream statistical literature the transition from inference to a formal decision model is seldom explicitly considered. Since the Markov chain Monte Carlo (MCMC) revolution in Bayesian statistics, focus has generally been on the development of novel algorithmic methods to enable comprehensive inference in a variety of applications, or to tackle realistic problems which naturally fit into the Bayesian paradigm. In this chapter, the primary focus is on the formal decision or optimization model. The statistical input needed to solve the decision problem is tackled via Bayesian parametric and semi-parametric models. The optimization/Bayesian models are then applied to solving an important problem in a nuclear power plant system at the South Texas Project (STP) Electric Generation Station. STP is one of the newest and largest nuclear power plants in the US, and is an industry leader in safety, reliability and efficiency. STP has two nuclear reactors that together can produce 2,500 megawatts of electric power. The reactors went online in August 1988 and June 1989, and are the sixth and fourth youngest, respectively, of more than 100 such reactors operating nationwide. STP consistently leads all US nuclear plants in the amount