Patent ID: 11861519
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. The system of claim 6,
wherein said local probability model assigned to each of said every symptom nose is defined by:
identifying a plurality of manifesting parent nodes such that each is a source node connected by a manifesting edge, representing a manifests relationship of the group of relationships, to the symptom node;
identifying a plurality of influencing-factor source nodes such that each is a source node connected by an influencing-factor edge, representing an influencing-factor relationship of the group of relationships, to the symptom node;
identifying a plurality of influencing combinations of a plurality of Boolean occurrence values of the plurality of influencing-factor source nodes; and
for each influencing combination of the plurality of influencing combinations:
associating with each manifesting source node of the plurality of manifesting source nodes a probability that a true Boolean occurrence value of the manifesting source node does not cause a true Boolean occurrence value of the symptom node, denoted by λw;
associating with the symptom node a probability that the symptom node has a true Boolean occurrence value when none of the plurality of manifesting source nodes has a true Boolean occurrence value, denoted by λ0; and
associating with the symptom node a noisy-or distribution computed by:
computing a plurality of node terms by for each of the plurality of manifesting source nodes having a true Boolean occurrence value subtracting the manifesting source node's λw from 1;
multiplying the plurality of node terms to produce a parent product;
computing an independent term by subtracting λ0 from 1;
multiplying the parent product by the independent term to produce a node product; and
subtracting the node product from 1; and

wherein training the statistical model comprises deriving values for a plurality of λw and λ0 values from the input data.