Bayes estimation of reliability under a random environment governed by a Dirichlet prior

The reliability function of a component whose lifetime is exponentially distributed with a known parameter λ>0 is R (t|λ)=exp (-λt). If an environmental effect multiplies the parameter by a positive factor η, then the reliability function becomes R(t|η,λ)=exp(-ηλt). The authors assume that η itself is random, and its uncertainty is described by a Dirichlet process prior D(α) with parameter α=MG₀, where M>O represents an intensity of assurance in the prior guess, G₀, of the (unknown) distribution of η. Under squared error loss, the Bayes estimator of R(t|η,λ) is derived both for the no-sample problem and for a sample of size n. Using Monte Carlo simulation, the effects of n, M, G₀ on the estimator are studied. These examples show that: (a) large values of n lead to estimates where the data outweigh the prior, and (b) large values of M increase the contribution of the prior to the estimates. These simulation results support intuitive ideas about the effect of environment and lifetime parameters on reliability