Open AccessDynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Statistics Base, Matrix, and Application
Author(s) -
Qin Zhang,
Chunling Dong,
Yan Cui,
Zhihui Yang
Publication year - 2014
Publication title -
ieee transactions on neural networks and learning systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.882
H-Index - 212
eISSN - 2162-2388
pISSN - 2162-237X
DOI - 10.1109/tnnls.2013.2279320
Subject(s) - computing and processing , communication, networking and broadcast technologies , components, circuits, devices and systems , general topics for engineers
Graphical models for probabilistic reasoning are now in widespread use. Many approaches have been developed such as Bayesian network. A newly developed approach named as dynamic uncertain causality graph (DUCG) is initially presented in a previous paper, in which only the inference algorithm in terms of individual events and probabilities is addressed. In this paper, we first explain the statistic basis of DUCG. Then, we extend the algorithm to the form of matrices of events and probabilities. It is revealed that the representation of DUCG can be incomplete and the exact probabilistic inference may still be made. A real application of DUCG for fault diagnoses of a generator system of a nuclear power plant is demonstrated, which involves variables. Most inferences take with a laptop computer. The causal logic between inference result and observations is graphically displayed to users so that they know not only the result, but also why the result obtained.