1. A method for forming an enhanced version of a belief network for subsequent use in assisting a user, through computer-aided probabilistic inferences, in a decision-making process, the method being implemented 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 the enhanced version of the belief network, the method comprising the steps, implemented by the processor through execution of the instructions, of: receiving an initial version of the belief network, the belief network probabilistically relating a plurality of different input variables to a plurality of different output decisions, the initial version of the belief network having network nodes each with a probability and each having a decision graph data structure with a graph node used for storing the probability; for each network node in the initial belief network: accessing a database containing cases of real-world instances of the decision-making process, each case containing a value for the graph node of the decision graph; counting a number of values for the graph node contained in the cases; for each graph node that is a leaf graph node in the graph data structure, performing a complete split on the leaf graph node to generate a plurality of complete split decision graphs; performing a binary split on the leaf graph node to generate a plurality of binary split decision graphs; and performing a merge on the leaf graph node to generate a plurality of merge decision graphs; scoring each of the complete split decision graphs, the binary split decision graphs, and the merge decision graphs for goodness at reflecting the cases using the counted number of values; determining which among the complete split decision graphs, the binary split decision graphs, and the merge decision graphs is a graph with a greatest score and retaining the graph with the greatest score so as to define a retained graph; determining which network node is a best network node having the retained graph with a best score among the retained graphs; storing the retained graph of the best network node into the best network node for use in accessing the probability of the best network node; and adjusting the initial version of the belief network responsive to storing the retained graph to create the enhanced version of the belief network for storage in the data structure in the storage device for subsequent use in assisting the user, through the computer-aided probabilistic inferences, with the decision-making process.
1. A method for forming an enhanced version of a belief network for subsequent use in assisting a user, through computer-aided probabilistic inferences, in a decision-making process, the method being implemented 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 the enhanced version of the belief network, the method comprising the steps, implemented by the processor through execution of the instructions, of: receiving an initial version of the belief network, the belief network probabilistically relating a plurality of different input variables to a plurality of different output decisions, the initial version of the belief network having network nodes each with a probability and each having a decision graph data structure with a graph node used for storing the probability; for each network node in the initial belief network: accessing a database containing cases of real-world instances of the decision-making process, each case containing a value for the graph node of the decision graph; counting a number of values for the graph node contained in the cases; for each graph node that is a leaf graph node in the graph data structure, performing a complete split on the leaf graph node to generate a plurality of complete split decision graphs; performing a binary split on the leaf graph node to generate a plurality of binary split decision graphs; and performing a merge on the leaf graph node to generate a plurality of merge decision graphs; scoring each of the complete split decision graphs, the binary split decision graphs, and the merge decision graphs for goodness at reflecting the cases using the counted number of values; determining which among the complete split decision graphs, the binary split decision graphs, and the merge decision graphs is a graph with a greatest score and retaining the graph with the greatest score so as to define a retained graph; determining which network node is a best network node having the retained graph with a best score among the retained graphs; storing the retained graph of the best network node into the best network node for use in accessing the probability of the best network node; and adjusting the initial version of the belief network responsive to storing the retained graph to create the enhanced version of the belief network for storage in the data structure in the storage device for subsequent use in assisting the user, through the computer-aided probabilistic inferences, with the decision-making process. 4. The method of claim 1 wherein the adjusting step includes adding an arc such that a cycle is introduced into the enhanced version of the belief network.