Belief updating and learning in semi qualitative probabilistic networks

Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available.

A conventional way to address this challenge is to introduce domain knowledge/expert judgments that are encoded as qualitative parameter constraints.

This work was supported by the National Natural Science Foundation of China (No.

60763007), the Natural Science Foundation of Yunnan Province (No.

To exploit expert knowledge about such constraints we have developed an improved constrained optimization algorithm, which achieves good parameter learning performance using these constraints, especially when data are limited.

Specifically, this algorithm outperforms the previous state-of-the-art and is also robust to errors in labelling the monotonic influences.

He has held previous academic posts at City University (professor in Centre for Software Reliability), South Bank (director of Centre for Systems and Software Engineering), Oxford University and University College Dublin (both as research fellow), and was a visiting researcher at GMD in Germany.

He is a chartered engineer and a chartered mathematician.

belief updating and learning in semi qualitative probabilistic networks-12belief updating and learning in semi qualitative probabilistic networks-66

In this paper, we first extend the traditional definition of qualitative influences by adopting the probabilistic threshold.He has published several papers in reputed journals and conferences in this area, including IJAR, UAI, and PGM.Norman Fenton is a professor of computer science at Queen Mary, University of London, and is also a chief executive officer of Agena Ltd., a company that specialises in risk management and decision support.This paper investigates probabilistic logics endowed with independence relations.We review propositional probabilistic languages without independence, and then consider graph-theoretic representations for propositional probabilistic logic with independence; complexity is analyzed, algorithms are derived, and examples are discussed.

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