Inference in continuous-time change-point models
Florian Stimberg, Andreas Ruttor, Manfred Opper and Guido Sanguinetti
In: NIPS, 12-15 Dec 2011, Granada, Spain.
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.