  We consider Bayesian nonparametric inference in the right-censoring survival
model, where modeling is made at the level of the hazard rate. We derive
posterior limiting distributions for linear functionals of the hazard, and then
for `many' functionals simultaneously in appropriate multiscale spaces. As an
application, we derive Bernstein-von Mises theorems for the cumulative hazard
and survival functions, which lead to asymptotically efficient confidence bands
for these quantities. Further, we show optimal posterior contraction rates for
the hazard in terms of the supremum norm. In medical studies, a popular
approach is to model hazards a priori as random histograms with possibly
dependent heights. This and more general classes of arbitrarily smooth prior
distributions are considered as applications of our theory. A sampler is
provided for possibly dependent histogram posteriors. Its finite sample
properties are investigated on both simulated and real data experiments.
