Abstract:
This paper addresses the iterative optimization of discrete probability distributions using a information geometry framework. Discrete probability distributions can be re...Show MoreMetadata
Abstract:
This paper addresses the iterative optimization of discrete probability distributions using a information geometry framework. Discrete probability distributions can be represented both as a mixture family or an exponential family. A Riemannian metric is introduced in these spaces given by the Fisher information matrix. The natural gradient is then computed with respect to this metric and is used in a iterative procedure for optimization. Properties of both formulations are given, and examples are presented. Finally, the formulation is illustrated in a probabilistic control design for a gene regulatory network problem.
Published in: 2009 European Control Conference (ECC)
Date of Conference: 23-26 August 2009
Date Added to IEEE Xplore: 02 April 2015
Print ISBN:978-3-9524173-9-3