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.

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 method and system for detecting and monitoring discrete interactions within a physical social network is provided. The system and method detects attributes associated with human communication activities and distinguishes them from other concurrently detected activities in order to identify discrete interactions that are in turn transmitted across a network having a limited data rate. Generally, in its simplest form a plurality of wireless sensors are deployed across a cross-section of people of interest. Once deployed, the wireless sensors establish an ad-hoc network that transmits a small amount of data relating to the each of the discrete individuals bearing a sensor. The collected data is analyzed through the application of rules set forth in a Bayesian belief network to make a determination regarding the probability that an actual interaction between two individuals bearing sensors in fact occurred.

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.

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.