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 |
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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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