A method of automatically constructing probabilistic graphical models from a source of data for user selection includes: providing in memory a predefined catalog of graphical model structures based on node types and relations among node types; selecting by user input specified node types and relations; automatically creating, in a processor, model structures from the predefined catalog of graphical model structures and the source of data based on user selected node types and relations; automatically evaluating, in the processor, the created model structures based on a predefined metric; automatically building, in the processor, a probabilistic graphical model for each created model structure based on the evaluations; calculating a value of the predefined metric for each probabilistic graphical model; scoring each probabilistic graphical model based on the calculated metric; and presenting to the user each probabilistic graphical model with an associated score for selection by the user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of automatically constructing probabilistic graphical models from a source of data in a memory location for user selection, the method comprising: providing in memory a predefined catalog of graphical model structures based on node types and relations among node types; selecting by user input specified node types and relations; automatically creating, in a processor, model structures from the predefined catalog of graphical model structures and the source of data based on user selected node types and relations; automatically evaluating, in the processor, the created model structures based on a predefined metric; automatically building, in the processor, a probabilistic graphical model for each created model structure based on the evaluations; calculating a value of the predefined metric for each probabilistic graphical model; scoring each probabilistic graphical model based on the calculated metric; and presenting to the user each probabilistic graphical model with an associated score for selection by the user. 2. The method of claim 1, further comprising automatically generating, in a processor, variants of the created model structures. 3. The method of claim 2, wherein automatically generating variants of the model structures includes explicit model variation. 4. The method of claim 3, wherein the explicit model variation includes varying the number of mixture components of a created model structure. 5. The method of claim 2, to wherein automatically generating variants of the model structures includes implicit model variation. 6. The method of claim 5, wherein the implicit model variation includes at least one of a divorcing, noisy-OR, or a noisy-AND structure alteration technique. 7. The method of claim 1, wherein the created model structure is one of a Gaussian Mixture Model or a Hidden Markov Model. 8. The method of claim 1, further comprising training the created model structure. 9. The method of claim 8, wherein training the created model structure includes using a training algorithm specifically modified for a graphical model structure of the predefined catalog. 10. The method of claim 1 wherein scoring each probabilistic graphic model is performed by cross-validation.