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Inference in continuous-time change-point models
Citation key Stimberg:2011:ICT
Author Florian Stimberg and Manfred Opper and Guido Sanguinetti and Andreas Ruttor
Title of Book Advances in Neural Information Processing Systems 24
Pages 2717–2725
Year 2011
Editor J. Shawe-Taylor and R.S. Zemel and P. Bartlett and F.C.N. Pereira and K.Q. Weinberger
Abstract 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.
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