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Bayesian Inference for Change Points in Dynamical Systems with Reusable States—a Chinese Restaurant Process Approach
Citation key Stimberg:2012:BIC
Author Florian Stimberg and Manfred Opper and Andreas Ruttor
Title of Book Proceedings of the International Conference on Artificial Intelligence and Statistics
Year 2012
ISSN 1938-7228
Journal JMLR W&CP
Volume 22
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.
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