Probabilistic graphs using coupled random variables

Kenric P. Nelson; Madalina Barbu; Brian J. Scannell

doi:10.1117/12.2050759

22 May 2014 Probabilistic graphs using coupled random variables

Kenric P. Nelson, Madalina Barbu, Brian J. Scannell

Proceedings Volume 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII; 911903 (2014) https://doi.org/10.1117/12.2050759
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

Neural network design has utilized flexible nonlinear processes which can mimic biological systems, but has suffered from a lack of traceability in the resulting network. Graphical probabilistic models ground network design in probabilistic reasoning, but the restrictions reduce the expressive capability of each node making network designs complex. The ability to model coupled random variables using the calculus of nonextensive statistical mechanics provides a neural node design incorporating nonlinear coupling between input states while maintaining the rigor of probabilistic reasoning. A generalization of Bayes rule using the coupled product enables a single node to model correlation between hundreds of random variables. A coupled Markov random field is designed for the inferencing and classification of UCI’s MLR ‘Multiple Features Data Set’ such that thousands of linear correlation parameters can be replaced with a single coupling parameter with just a (3%, 4%) reduction in (classification, inference) performance.

Citation Download Citation

Kenric P. Nelson, Madalina Barbu, and Brian J. Scannell "Probabilistic graphs using coupled random variables", Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 911903 (22 May 2014); https://doi.org/10.1117/12.2050759

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