Inference in continuous-time change-point models

NeurIPS 2011 Florian StimbergManfred OpperGuido SanguinettiAndreas Ruttor

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... (read more)

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