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 Xi on 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.

Abstract: A system for computing probabilities of variables in a belief network includes a data acquisition interface configured to receive data representative of the belief network. The system further includes a partial evaluator configured to carry out a partial evaluation algorithm that determines the probability calculations that must be performed on the received data in order to compute the probabilities of the variables in the belief network. The system further includes a source code generator configured to output the probability calculations as a source code in a programming language.

Abstract: 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 Xi (i=1, . . . , n) for the child node Y. The causal influence model describes the influence of the parent nodes Xi on 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 Xi.

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 Xi on 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.

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 Xi on 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.

Abstract: A system for computing probabilities of variables in a belief network includes a data acquisition interface configured to receive data representative of the belief network. The system further includes a partial evaluator configured to carry out a partial evaluation algorithm that determines the probability calculations that must be performed on the received data in order to compute the probabilities of the variables in the belief network. The system further includes a source code generator configured to output the probability calculations as a source code in a programming language.

Abstract: A system for computing probabilities of variables in a belief network includes a data acquisition interface configured to receive data representative of the belief network. The system further includes a partial evaluator configured to carry out a partial evaluation algorithm that determines the probability calculations that must be performed on the received data in order to compute the probabilities of the variables in the belief network. The system further includes a source code generator configured to output the probability calculations as a source code in a programming language.

Abstract: 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.

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 Xi on 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.

Abstract: 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 Xi (i=1, . . . , n) for the child node Y. The causal influence model describes the influence of the parent nodes Xi on 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 Xi.