Abstract by Jackson Curtis
An R package for Nonparametric Bayesian System Reliability
Bayesian nonparametric analysis is a good candidate for estimating system reliability because of its flexibility and uncertainty quantification. The beta-Stacy process can be used to provide a nonparametric conjugate model for estimating reliability. The process is an extension of a Dirichlet process but is adjusted to handle right censored data and shares properties with the frequentist Kaplan-Meier estimator. In this talk, I will demonstrate an R package to create beta-Stacy process objects, analyze system reliability, and visualize the results.