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Bayesian nonparametric modelling with the Dirichlet process regression smoother

Steel, M (Warwick)
Wednesday 08 August 2007, 17:30-18:30

Seminar Room 1, Newton Institute


In this paper we discuss the problem of Bayesian fully nonparametric regression. A new construction of priors for nonparametric regression is discussed and a specific prior, the Dirichlet Process Regression Smoother, is proposed. We consider the problem of centring our process over a class of regression models and propose fully nonparametric regression models with flexible location structures. Computational methods are developed for all models described. Results are presented for simulated and actual data examples.

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