Abstract
Bayesian-directed acyclic discrete-variable graphs are reduced to a simplified normal form made up of only replicator units (or equal constraint units), source, and single-input/single-output blocks. In this framework, the same adaptation algorithm can be applied to all the parametric blocks. We obtain and compare adaptation rules derived from a constrained maximum likelihood formulation and a minimum Kullback-Leibler divergence criterion using Karush-Kuhn-Tucker conditions. The learning algorithms are compared with two other updating equations based on localized decisions and on a variational approximation, respectively. The performance of the various algorithms is verified on synthetic data sets for various architectures. Factor graphs in reduced normal form provide an appealing framework for rapid deployment of Bayesian-directed graphs in the applications.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Neural Networks and Learning Systems
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.
