Charles River Analytics, Inc.

David Koelle

Zachary T Cox

Jonathan Pfautz

Joseph Campolongo

Geoffrey Catto

Methods and systems are described for simplifying a causal influence model that describes influence of parent nodes X(i=1, . . . , n) on possible states of the child node Y. The child node Y and each one of the parent nodes X(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 Xon 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.

Methods and systems for constructing Bayesian belief networks

An apparatus for making probabilistic inferences based on a belief network includes a processing system configured to receive as input one or more parameters of a causal influence model. The belief network has a child node Y and one or more parent nodes X(i=1, . . . , n) for the child node Y. The causal influence model describes the influence of the parent nodes Xon possible states of the child node Y. The processing system is further configured to use a creation function to convert the parameters of the causal influence model into one or more entries of a conditional probability table. The conditional probability table provides a probability distribution for all the possible states of the child node Y, for each combination of possible states of the parent nodes X.

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Arlington, MA, United States of America

David Koelle