Griffin, J.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.
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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.
|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:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Jim Griffin|
|Date Deposited:||30 May 2012 10:05|
|Last Modified:||06 Jun 2012 11:21|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/29592 (The current URI for this page, for reference purposes)|
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