Systems, methods, and computer program products for predictive accuracy for strategic decision support
First Claim
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1. A method, comprising:
- providing an unconstrained domain model defined by domain concepts, probabilistic causal relationships between the domain concepts, and a temporal relationship between at least one pair of the domain concepts, wherein each probabilistic causal relationship includes a value for the weight of causal belief for the probabilistic causal relationship, and wherein each temporal relationship includes a value for the expected time to the effect for the temporal relationship;
receiving the probabilistic causal relationships between the domain concepts for the domain model from a user;
receiving the temporal relationships between the at least one pair of domain concepts for the domain from the user; and
transforming, using a processor, the unconstrained domain model into a Continuous Time Bayesian Network formalism for the domain model using at least the domain concepts and temporal relationships.
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Abstract
Provided are improved systems, methods, and computer programs to facilitate predictive accuracy for strategic decision support using Continuous Time Bayesian Networks. A simple semantic is provided for both probabilistic and temporal relationships that can be automatically translated into functioning Bayesian and Continuous Time Bayesian Network formalisms and applied by a user for reasoning analysis, such as to assess a hypothesis or respond to a query with a probabilistic and temporal prediction of a future event.
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Citations
17 Claims
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1. A method, comprising:
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providing an unconstrained domain model defined by domain concepts, probabilistic causal relationships between the domain concepts, and a temporal relationship between at least one pair of the domain concepts, wherein each probabilistic causal relationship includes a value for the weight of causal belief for the probabilistic causal relationship, and wherein each temporal relationship includes a value for the expected time to the effect for the temporal relationship; receiving the probabilistic causal relationships between the domain concepts for the domain model from a user; receiving the temporal relationships between the at least one pair of domain concepts for the domain from the user; and transforming, using a processor, the unconstrained domain model into a Continuous Time Bayesian Network formalism for the domain model using at least the domain concepts and temporal relationships. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system, comprising:
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an unconstrained domain model defined by domain concepts, probabilistic causal relationships between the domain concepts, and a temporal relationship between at least one pair of the domain concepts, wherein each probabilistic causal relationship includes a value for the weight of causal belief for the probabilistic causal relationship, and wherein each temporal relationship includes a value for the expected time to the effect for the temporal relationship; a reasoning tool configured to perform reasoning analysis according to a hypothesis or a query using the Bayesian network formalism and the Continuous Time Bayesian Network formalism for the domain model; an output tool configured to output a prediction of the probability of the timing for an event based upon the hypothesis or query; and a processor configured to transform the unconstrained domain model into a Continuous Time Bayesian Network formalism for the domain model using at least the domain concepts and temporal relationships. - View Dependent Claims (13, 14)
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15. A computer program comprising a computer-useable memory having control logic stored therein for facilitating predictive accuracy for strategic decision support, the control logic comprising:
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a first code configured to provide an unconstrained domain model defined by domain concepts, probabilistic causal relationships between the domain concepts, and a temporal relationship between at least one pair of the domain concepts, wherein each probabilistic causal relationship includes a value for the weight of causal belief for the probabilistic causal relationship, and wherein each temporal relationship includes a value for the expected time to the effect for the temporal relationship; and a second code configured to transform the unconstrained domain model into a Continuous Time Bayesian Network formalism for the domain model using at least the domain concepts and temporal relationships, wherein the second code is further configured to transform the unconstrained domain model into a Continuous Time Bayesian Network formalism by creating a conditional intensity matrix for each temporal relationship. - View Dependent Claims (16, 17)
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Specification