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The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference

Griffin, Jim E. (2011) The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference. Journal of Statistical Planning and Inference, 141 (11). pp. 3648-3664. ISSN 0378-3758. (doi:10.1016/j.jspi.2011.05.019) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:29592)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1016/j.jspi.2011.05.019

Abstract

This paper introduces a new class of time-varying, measure-valued stochastic processes for Bayesian nonparametric inference. The class of priors is constructed by normalising a stochastic process derived from non-Gaussian Ornstein-Uhlenbeck processes and generalises the class of normalised random measures with independent increments from static problems. Some properties of the normalised measure are investigated. A particle filter and MCMC schemes are described for inference. The methods are applied to an example in the modelling of financial data.

Item Type: Article
DOI/Identification number: 10.1016/j.jspi.2011.05.019
Uncontrolled keywords: Normalised random measures with independent increments; Ornstein–Uhlenbeck process; Time-dependent Bayesian nonparametrics; Particle filtering; Dirichlet process
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Jim Griffin
Date Deposited: 30 May 2012 10:05 UTC
Last Modified: 16 Nov 2021 10:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/29592 (The current URI for this page, for reference purposes)

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