Convergence rates of posterior distributions for Brownian semimartingale models

None, None; None, None; None, None

Convergence rates of posterior distributions for Brownian semimartingale models

Journal Article (2006)

Author(s)

F. H. van der Meulen (Vrije Universiteit Amsterdam)

A. W. van der Vaart (Vrije Universiteit Amsterdam)

J. H. van Zanten (Vrije Universiteit Amsterdam)

Bayesian estimation Dirichlet process Wavelets Hellinger distance Continuous semimartingale Infinite-dimensional model Rate of convergence

DOI related publication

https://doi.org/10.3150/bj/1161614950 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:3bf2481b-c67a-4907-b970-f4fe9994c1bd

More Info

expand_more

Publication Year

2006

Language

English

Issue number

5

Volume number

12

Pages (from-to)

863-888

Downloads counter

152

Abstract

We consider the asymptotic behaviour of posterior distributions based on continuous observations from a Brownian semimartingale model. We present a general result that bounds the posterior rate of convergence in terms of the complexity of the model and the amount of prior mass given to balls centred around the true parameter. This result is illustrated for three special cases of the model: the Gaussian white noise model, the perturbed dynamical system and the ergodic diffusion model. Some examples for specific priors are discussed as well.