| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 415175 | Computational Statistics & Data Analysis | 2009 | 9 Pages |
Abstract
A Bayesian nonparametric approach to modeling a nonlinear dynamic model is presented. New techniques for sampling infinite mixture models are used. The inference procedure specifically in the case of the logistic model and when the nonparametric component is applied to the additive errors is demonstrated.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Spyridon J. Hatjispyros, Theodoros Nicoleris, Stephen G. Walker,