Graphical Models in Reconstructability Analysis and Bayesian Networks

Abstract

This research focuses on the investigation of two machine learning methodologies, Reconstructability Analysis (RA) and Bayesian Networks (BN). Both methods are probabilistic graphical modeling (PGM) methodologies. RA was developed in the systems community and has applications including time-series analysis, classification, decomposition, compression, pattern recognition, prediction, control, and decision analysis. BNs have origins in path models and have applications similar to those of RA. BNs are another graphical modeling approach for data modeling that is closely related to RA; where BN overlaps RA the two methods are equivalent, but RA and BN each has distinctive features absent in the other methodology.The primary aim of this research is to make theoretical contributions through the unification of the RA and BN methods by developing and integrating the RA and BN neutral and directed system lattices and developing an algorithm to generate the joint RA-BN neutral system lattice of structures for any number of variables. This analysis is done exhaustively for four variables, which is sufficient to elucidate the formal relationship between these two PGM approaches. The secondary aim of this research is to apply RA and BN to a real world problem in the electricity industry to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC and DEC Resource Sufficiency Requirements for Western Energy Imbalance Market participants.