Bayesian Inference for Change Points in Dynamical Systems with Reusable States - A Chinese Restaurant Process Approach
Abstract
We study a model of a stochastic process with unobserved parameters which suddenly change at random times. The possible parameter values are assumed to be from a finite but unknown set. Using a Chinese restaurant process prior over parameters we develop an efficient MCMC procedure for Bayesian inference. We demonstrate the significance of our approach with an application to systems biology data.
Cite
Text
Stimberg et al. "Bayesian Inference for Change Points in Dynamical Systems with Reusable States - A Chinese Restaurant Process Approach." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Stimberg et al. "Bayesian Inference for Change Points in Dynamical Systems with Reusable States - A Chinese Restaurant Process Approach." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/stimberg2012aistats-bayesian/)BibTeX
@inproceedings{stimberg2012aistats-bayesian,
title = {{Bayesian Inference for Change Points in Dynamical Systems with Reusable States - A Chinese Restaurant Process Approach}},
author = {Stimberg, Florian and Ruttor, Andreas and Opper, Manfred},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {1117-1124},
volume = {22},
url = {https://mlanthology.org/aistats/2012/stimberg2012aistats-bayesian/}
}