Inference in continuous-time change-point models

Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We consider the problem of Bayesian inference for continuous time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times. We propose exact inference and sampling methodologies for two specific cases where the discontinuous dynamics is given by a Poisson process and a two-state Markovian switch. We test the methodology on simulated data, and apply it to two real data sets in finance and systems biology. Our experimental results show that the approach leads to valid inferences and non-trivial insights.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 24
EditorsJ. Shawe-Taylor, R.S. Zemel, P.L. Bartlett, F. Pereira, K.Q. Weinberger
PublisherCurran Associates Inc
Number of pages9
Publication statusPublished - 2011


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