Methods and systems for constructing Bayesian belief networks

Details Methods and systems for constructing Bayesian belief networks Methods and systems for constructing Bayesian belief networks

2008-01-30

2011-12-13

Starks, Wilbert L (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

Knowledge representation and reasoning technique

C706S045000

Reexamination Certificate

active

08078566

ABSTRACT:
Methods and systems are described for simplifying a causal influence model that describes influence of parent nodes Xi(i=1, . . . , n) on possible states of the child node Y. The child node Y and each one of the parent nodes Xi(i=1, . . . , n) are assumed to be either a discrete Boolean node having states true and false, a discrete Ordinal node having a plurality of ordered states; and a Categorical node having a plurality of unordered states. The influence of each parent node Xion the child node Y is assumed to be a promoting influence and an inhibiting influence. User interfaces are described that incorporate these specific node types.

REFERENCES:
Falzon, et al, The Centre of Gravity Network Effects Tool: Probabilistic Modelling for Operational Planning, DSTO Information Sciences Laboratory, DSTO-TR-1604, 2004, pp. 1-44.

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