PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8819
Deposited By:Guido Sanguinetti
Deposited On:21 February 2012