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Bayesian nonparametrics with heterogeneous data

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The talk surveys some recent work on random probability measure vectors and their role in Bayesian statistics. Indeed, dependent nonparametric priors are useful tools for drawing inferences on data that arise from different studies or experiments and for which the usual exhangeability assumption is not satisfied. The presentation will focus on mixture models and their uses for density estimation and for the analysis of right-censored survival data. Some of the theoretical results to be presented are also relevant for devising Gibbs sampling schemes that will be applied to simulated and real datasets.

This talk is part of the Statistics series.

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