The Annals of Statistics

On rates of convergence for posterior distributions in infinite-dimensional models

Stephen G. Walker, Antonio Lijoi, and Igor Prünster

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This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. In particular, we improve on current rates of convergence for models including the mixture of Dirichlet process model and the random Bernstein polynomial model.

Article information

Ann. Statist., Volume 35, Number 2 (2007), 738-746.

First available in Project Euclid: 5 July 2007

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Zentralblatt MATH identifier

Primary: 62G07: Density estimation 62G20: Asymptotic properties 62F15: Bayesian inference

Hellinger consistency mixture of Dirichlet process posterior distribution rates of convergence


Walker, Stephen G.; Lijoi, Antonio; Prünster, Igor. On rates of convergence for posterior distributions in infinite-dimensional models. Ann. Statist. 35 (2007), no. 2, 738--746. doi:10.1214/009053606000001361.

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