8. In a computer system having a processor and a storage device connected to the processor, wherein the storage device stores computer executable instructions and a data structure, the data structure storing a belief network, wherein the belief network probabilistically relates a plurality of different input variables to a plurality of different output decisions, a method for use in the system for scoring the belief network, wherein the belief network has nodes and arcs indicating relationships between the nodes, a plurality of the nodes having decision graphs containing probabilities for rendering inferences when given an observation for another node, and the belief network provides probabilistic based decision-support to a user of the system in a field of decision making, comprising the steps, implemented through the computer executable instructions, of: accessing a database having observations for the nodes of the belief network representing instances of real-world decisions relevant to a predetermined field of decision making; scoring the decision graph in each node for goodness at reflecting the observations in the accessed database, wherein the decision graph reflects relationships among the nodes of the belief network, one of the reflected relationships being an equivalency relationship where a plurality of paths through the decision graph refer to a common probability, and wherein the scoring is performed to determine whether the observations indicate that the decision graph should reflect additional relationships; and combining the scores of the decision graphs to generate a score for the belief network, wherein the score reflects how accurately the belief network represents a decision making process between the input variables and the output decisions.
8. In a computer system having a processor and a storage device connected to the processor, wherein the storage device stores computer executable instructions and a data structure, the data structure storing a belief network, wherein the belief network probabilistically relates a plurality of different input variables to a plurality of different output decisions, a method for use in the system for scoring the belief network, wherein the belief network has nodes and arcs indicating relationships between the nodes, a plurality of the nodes having decision graphs containing probabilities for rendering inferences when given an observation for another node, and the belief network provides probabilistic based decision-support to a user of the system in a field of decision making, comprising the steps, implemented through the computer executable instructions, of: accessing a database having observations for the nodes of the belief network representing instances of real-world decisions relevant to a predetermined field of decision making; scoring the decision graph in each node for goodness at reflecting the observations in the accessed database, wherein the decision graph reflects relationships among the nodes of the belief network, one of the reflected relationships being an equivalency relationship where a plurality of paths through the decision graph refer to a common probability, and wherein the scoring is performed to determine whether the observations indicate that the decision graph should reflect additional relationships; and combining the scores of the decision graphs to generate a score for the belief network, wherein the score reflects how accurately the belief network represents a decision making process between the input variables and the output decisions. 9. The method of claim 8 wherein the scoring step includes scoring the decision graphs based on expert data received from an expert in the field of the decision making.