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2012 AISTATS AISTATS 2012

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐣 Hot Topic Early Bird — markov chain monte carlo
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Florian Stimberg , Andreas Ruttor , Manfred Opper

Topics

Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Optimization & Theory > Stochastic Processes Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Bayesian Inference

Keywords

change point detection bayesian inference markov chain monte carlo stochastic process chinese restaurant process systems biology dynamical system
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