  Dirichlet process mixture model (DPMM) is a popular Bayesian nonparametric
model. In this paper, we apply this model to weighted data and then estimate
the un-weighted distribution from the corresponding weighted distribution using
the metropolis-Hastings algorithm. We then apply the DPMM with different
kernels to simulated and real data sets. In particular, we work with lifetime
data in the presence of censored data and then calculate estimated density and
survival values.
