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Some good news about nonparametric priors in density estimation

Nieto-Barajas, LE (ITAM)
Tuesday 07 August 2007, 10:00-11:00

Seminar Room 1, Newton Institute


Bayesian nonparametric methods have recently gained popularity in the context of density estimation. In particular, the density estimator arising from the mixture of Dirichlet process (MDP) and the mixture of normalized inverse gaussian process are now commonly exploited in practice. We perform a sensitivity analysis for a wide class of Bayesian nonparametric density estimators by perturbing the prior itself by means of a suitable function. Our findings bring some clear evidence in favor of Bayesian nonparametric density estimators due to a neutralization of the perturbation in the posterior distribution.

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