Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

A variational approach to path estimation and parameter inference of hidden diffusion processes

Author(s):

T. Sutter, A. Ganguly, H. Koeppl
Conference/Journal:

Journal of Machine Learning Research, vol. 17, pp. 37, (arXiv 1508.00506)

[OC:03715]
Abstract:

We consider a hidden Markov model, where the signal process, given by a diffusion, is only indirectly observed through some noisy measurements. The article develops a variational method for approximating the hidden states of the signal process given the full set of observations. This, in particular, leads to systematic approximations of the smoothing densities of the signal process. The paper then demonstrates how an efficient inference scheme, based on this variational approach to the approximation of the hidden states, can be designed to estimate the unknown parameters of stochastic differential equations. Two examples at the end illustrate the efficacy and the accuracy of the presented method.

Further Information
Year:

2016
Type of Publication:

(01)Article
Supervisor:



File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@Article { SutGan:2016:IFA_5211,
    author={T. Sutter and A. Ganguly and H. Koeppl},
    title={{A variational approach to path estimation and parameter
	  inference of hidden diffusion processes}},
    journal={Journal of Machine Learning Research},
    year={2016},
    volume={17},
    number={},
    pages={37},
    month=oct,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=5211}
}
Permanent link