Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations


This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.

Publication DOI:
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: © American Physical Society Funding: European FP7 grant under the GeoViQua project (Environment; 265178), and under the CompLACS grant (ICT; 270327)
Uncontrolled Keywords: Condensed Matter Physics,Statistical and Nonlinear Physics,Statistics and Probability
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://journals ... sRevE.91.012148 (Publisher URL)
PURE Output Type: Article
Published Date: 2015-01-30
Authors: Vrettas, Michail D.
Opper, Manfred
Cornford, Dan ( 0000-0001-8787-6758)



Version: Accepted Version


Version: Published Version

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