Nonparametric Bayes estimation in repair models

In this paper, we define a general model for dependent random variables taking values in a general space, which includes most of the repair models in the literature. We describe nonparametric Bayesian methods to estimate P, without making any assumptions on when we stop collecting data. To do this we introduce a new class of priors called partition-based (PB) priors and show that it is a conjugate class to a large class of our general repair models. We also define a subclass of such priors called partition-based Dirichlet (PBD) priors which also forms a conjugate family of priors. For a special case of the repair model called the aging repair model, we obtain an easily computable Bayes estimate of P under a Dirichlet prior. The Bayes estimates are smoother than Whitaker and Samaniego non-Bayes estimates. Graphical comparisons show that the Bayes and non-Bayes estimates tend to be close.