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Dirichlet process mixture model (DPMM) is a popular Bayesian nonparametricmodel. In this paper, we apply this model to weighted data and then estimatethe un-weighted distribution from the corresponding weighted distribution usingthe metropolis-Hastings algorithm. We then apply the DPMM with differentkernels to simulated and real data sets. In particular, we work with lifetimedata in the presence of censored data and then calculate estimated density andsurvival values.

Bayesian Nonparametric Model for Weighted Data Using Mixture of Burr XII Distributions