Provided are improved systems, methods, and computer programs to facilitate predictive accuracy for strategic decision support using a query specific Bayesian network. An unconstrained domain model is defined by domain concepts and causal relationships between the domain concepts. Each causal relationship includes a value for the weight of causal belief for the causal relationship. In response to a query, the unconstrained domain model is transformed into a query specific Bayesian network for the domain model by identifying one or more cycles in the unconstrained domain model, eliminating the one or more cycles from the unconstrained domain model; identifying a sub-graph of the unconstrained domain model that is relevant to a query and creating one or more conditional probability tables that comprise the query specific Bayesian network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: providing an unconstrained domain model defined by domain concepts and causal relationships between the domain concepts, wherein each causal relationship includes a value for the weight of causal belief for the causal relationship; and transforming, using processing circuitry and in response to a query, the unconstrained domain model into a query specific Bayesian network for the domain model by identifying one or more cycles in the unconstrained domain model, eliminating the one or more cycles from the unconstrained domain model; identifying a sub-graph of the unconstrained domain model that is relevant to the query and creating one or more conditional probability tables that comprise the query specific Bayesian network. 2. The method of claim 1, wherein identifying the sub-graph of the unconstrained domain model comprises pruning domain concepts that are irrelevant to the query. 3. The method of claim 1 wherein identifying the sub-graph of the unconstrained domain model comprises identifying the domain concepts that are relevant to the query. 4. The method of claim 1, wherein eliminating the one or more cycles comprises identifying the causal relationships between the domain concepts that are weak and removing the causal relationships between the domain concepts that are identified to be weak. 5. The method of claim 1, wherein identifying one or more cycles in the unconstrained domain model comprises identifying all cycles in the unconstrained domain model, and wherein eliminating the one or more cycles from the unconstrained domain model comprises eliminating all cycles from the unconstrained domain model. 6. The method of claim 1, wherein creating one or more conditional probability tables comprises creating a conditional probability table defining the causal relationship from a parent domain concept to a child domain concept by determining a probability of occurrence for a respective transition from each state of the parent domain concept to each state of the child domain concept. 7. The method of claim 1, wherein creating one or more conditional probability tables comprises creating a conditional probability table defining the causal relationship from a plurality of parent domain concepts to a child domain concept by determining a probability of occurrence for a respective transition from each combination of states of the parent domain concepts to each state of the child domain concept. 8. A system, comprising: a memory configured to provide an unconstrained domain model defined by domain concepts and causal relationships between the domain concepts, wherein each causal relationship includes a value for the weight of causal belief for the causal relationship; and processing circuitry configured to transform, in response to a query, the unconstrained domain model into a query specific Bayesian network for the domain model by identifying one or more cycles in the unconstrained domain model, eliminating the one or more cycles from the unconstrained domain model; identifying a sub-graph of the unconstrained domain model that is relevant to the query and creating one or more conditional probability tables that comprise the query specific Bayesian network. 9. The system of claim 8, wherein the processing circuitry is configured to identify the sub-graph of the unconstrained domain model by pruning domain concepts that are irrelevant to the query. 10. The system of claim 8 wherein the processing circuitry is configured to identify the sub-graph of the unconstrained domain model by identifying the domain concepts that are relevant to the query. 11. The system of claim 8, wherein the processing circuitry is configured to eliminate the one or more cycles by identifying the causal relationships between the domain concepts that are weak and removing the causal relationships between the domain concepts that are identified to be weak. 12. The system of claim 8, wherein the processing circuitry is configured to identify one or more cycles in the unconstrained domain model by identifying all cycles in the unconstrained domain model, and wherein the processing circuitry is configured to eliminate the one or more cycles from the unconstrained domain model by eliminating all cycles from the unconstrained domain model. 13. The system of claim 8, wherein the processing circuitry is configured to create one or more conditional probability tables by creating a conditional probability table defining the causal relationship from a parent domain concept to a child domain concept by determining a probability of occurrence for a respective transition from each state of the parent domain concept to each state of the child domain concept. 14. The system of claim 8, wherein the processing circuitry is configured to create one or more conditional probability tables by creating a conditional probability table defining the causal relationship from a plurality of parent domain concepts to a child domain concept by determining a probability of occurrence for a respective transition from each combination of states of the parent domain concepts to each state of the child domain concept. 15. A computer program product comprising a non-transitory computer-useable medium having control logic stored therein, the control logic comprising: a first code configured to providing an unconstrained domain model defined by domain concepts and causal relationships between the domain concepts, wherein each causal relationship includes a value for the weight of causal belief for the causal relationship; and a second code configured to transform, in response to a query, the unconstrained domain model into a query specific Bayesian network for the domain model by identifying one or more cycles in the unconstrained domain model, eliminating the one or more cycles from the unconstrained domain model; identifying a sub-graph of the unconstrained domain model that is relevant to the query and creating one or more conditional probability tables that comprise the query specific Bayesian network. 16. The computer program product of claim 15, wherein the second code is configured to identify the sub-graph of the unconstrained domain model by pruning domain concepts that are irrelevant to the query. 17. The computer program product of claim 15, wherein the second code is configured to eliminate the one or more cycles by identifying the causal relationships between the domain concepts that are weak and removing the causal relationships between the domain concepts that are identified to be weak. 18. The computer program product of claim 15, wherein the second code is configured to identify one or more cycles in the unconstrained domain model by identifying all cycles in the unconstrained domain model, and wherein the second code is configured to eliminate the one or more cycles from the unconstrained domain model by eliminating all cycles from the unconstrained domain model. 19. The computer program product of claim 15, wherein the second code is configured to create one or more conditional probability tables by creating a conditional probability table defining the causal relationship from a parent domain concept to a child domain concept by determining a probability of occurrence for a respective transition from each state of the parent domain concept to each state of the child domain concept. 20. The computer program product of claim 15, wherein the second code is configured to create one or more conditional probability tables by creating a conditional probability table defining the causal relationship from a plurality of parent domain concepts to a child domain concept by determining a probability of occurrence for a respective transition from each combination of states of the parent domain concepts to each state of the child domain concept.