Charles River Analytics, Inc.

Jonathan Pfautz

Zachary T Cox

David Koelle

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.

Conditional probability tables for Bayesian belief networks

An application for developing and using a model of a Bayesian Network to compute beliefs. The application provides an interface through which a user may specify the construction of the Bayseian Network, such as by specifying nodes in the network, parameters associated with the nodes, conditional probability distributions associated with the parameters or evidence that a parameter has a particular value. The application builds an inference engine based on user input specifying the construction of the Bayesian Network and uses it to compute beliefs. The application provides a user interface through which a user may specify the construction of the Bayesian Network and automatically updates an output reflecting beliefs. The input and output information may be available to the user simultaneously without switching operating modes of the application.

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Carlisle, MA, United States of America

Jonathan Pfautz