  We introduce a novel procedure to perform Bayesian non-parametric inference
with right-censored data, the \emph{beta-Stacy bootstrap}. This approximates
the posterior law of summaries of the survival distribution (e.g. the mean
survival time). More precisely, our procedure approximates the joint posterior
law of functionals of the beta-Stacy process, a non-parametric process prior
that generalizes the Dirichlet process and that is widely used in survival
analysis. The beta-Stacy bootstrap generalizes and unifies other common
Bayesian bootstraps for complete or censored data based on non-parametric
priors. It is defined by an exact sampling algorithm that does not require
tuning of Markov Chain Monte Carlo steps. We illustrate the beta-Stacy
bootstrap by analyzing survival data from a real clinical trial.
