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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.