Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach

Florian Stimberg, Andreas Ruttor, Manfred Opper
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1117-1124, 2012.

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 this Paper

BibTeX
@InProceedings{pmlr-v22-stimberg12, 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}, pages = {1117--1124}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/stimberg12/stimberg12.pdf}, url = {https://proceedings.mlr.press/v22/stimberg12.html}, 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.} }
Endnote
%0 Conference Paper %T Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach %A Florian Stimberg %A Andreas Ruttor %A Manfred Opper %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-stimberg12 %I PMLR %P 1117--1124 %U https://proceedings.mlr.press/v22/stimberg12.html %V 22 %X 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.
RIS
TY - CPAPER TI - Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach AU - Florian Stimberg AU - Andreas Ruttor AU - Manfred Opper BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-stimberg12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 1117 EP - 1124 L1 - http://proceedings.mlr.press/v22/stimberg12/stimberg12.pdf UR - https://proceedings.mlr.press/v22/stimberg12.html AB - 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. ER -
APA
Stimberg, F., Ruttor, A. & Opper, M.. (2012). 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, in Proceedings of Machine Learning Research 22:1117-1124 Available from https://proceedings.mlr.press/v22/stimberg12.html.

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