A system and method for utilizing a logical graphical model for data analysis are described. The system provides a “PGM authoring tool” that enables a user to employ a logical graphical model to create, edit, and browse the assertions and inferences in a probabilistic graphical model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for analyzing a probabilistic graphical model, comprising: retrieving a logical graph model, the logical graph model having one or more variables wherein each variable is represented as a logical class with attributes, wherein the logical graph model includes the variables of a probabilistic graphical model; selecting a variable in the logical graph model and comparing the selected variable to other variables in the logical graph model; generating, for one or more variables in the logical graph model, a value subclass for each of the variable's scenarios when the user asserts discrete scenarios for a variable; generating, for each template variable, index subclasses, each index subclass having at least one attribute comprised of a property and an object, the latter a subclass of the index object class; asserting at least one scenario or factor for a variable; creating edges with labels between the variables of the logical graph model, wherein the labeled edges correspond to influence relationships in the probabilistic graphical model, wherein the labels include “influences” and “influenced by” inverse properties; and creating additional index subclasses for one or more variables of the logical graph model, wherein the additional index subclasses of the one or more variables are influenced by additional index subclasses of one or more other variables in the probabilistic graphical model. 2. The method of claim 1, wherein asserting at least one scenario further comprises attaching evidence to at least one scenario for the variable. 3. The method of claim 1, wherein generating the value subclass for each variable for each scenario further comprises inheriting one or more attributes of the variable using logical inference. 4. The method of claim 1, wherein generating the variable or index subclass further comprises generating a confidence for each variable or index subclass. 5. The method of claim 4, wherein generating the confidence for each variable or index subclass further comprises combining an incremental confidence of the variable or index subclass and the confidence for each of the other variables or other index subclasses that the variable or index subclass is influenced by transitively. 6. The method of claim 1 further comprising automatically wiring together the index subclasses to each other using edges with labels, like the inverse properties “influences” and “influenced by”. 7. The method of claim 1 further comprising automatically asserting value subclasses for each variable or index subclass to represent scenarios for the variable or index subclass generated by probabilistic inference. 8. The method of claim 7 further comprising automatically asserting one or more attributes for the value subclasses. 9. The method of claim 8, wherein the one or more attributes for the value subclasses are one of a percentile in a cumulative probability distribution, a bin in a probability density function, a sample number among a series of samples, or whether its value is a statistical summary measure for the variable's scenarios, such as min, max, mean, skew, and kurtosis. 10. A system for analyzing a probabilistic graphical model, comprising: a store that stores a plurality of graphs including a logical graphical model, the logical graph model having one or more variables wherein each variable has an attribute, wherein the logical graph model includes the variables of a probabilistic graphical model; a processor and a memory; application logic executed by the processor so that the processor is configured to retrieve a logical graph model, select a variable in the logical graph model and compare the selected variable to other variables in the logical graph model, generate, for one or more variables in the logical graph model, value subclasses for each of the variable's scenarios when a user asserts discrete scenarios for a variable, generate, for each template variable, index subclasses, each index subclass having at least one attribute comprised of a property and an object, the latter a subclass of the index object class, assert at least one scenario for a variable, create edges with labels between the variables of the logical graph model, wherein the labeled edges correspond to influence relationships in the probabilistic graphical model, wherein the labels include “influences” and “influenced by” inverse properties and create additional index subclasses for one or more variables of the logical graph model, wherein the additional index subclasses of the one or more variables to influenced by additional index subclasses of one or more other variables in the probabilistic graphical model. 11. The system of claim 10, wherein the processor is configured to attach evidence to the variable or to one or more scenarios for the variable. 12. The system of claim 10, wherein the processor is configured to inherit one or more attributes of the variable using logical inference. 13. The system of claim 10, wherein the processor is configured to generate a confidence for each variable or index subclass. 14. The system of claim 13, wherein the processor is configured to combine an incremental confidence of the variable or index subclass and the confidence for each of the other variables or other index subclasses that the subclass is influenced by to generate the confidence. 15. The system of claim 10, wherein the processor is configured to automatically wire together the index subclasses to each other using edges with labels, like the inverse properties “influences” and “influenced by”. 16. The system of claim 10, wherein the processor is configured to automatically assert value subclass for each variable or index subclass to represent scenarios for the variable or index subclass generated by probabilistic inference. 17. The system of claim 16, wherein the processor is configured to automatically assert one or more attributes for the value subclasses. 18. The system of claim 17, wherein the one or more attributes for the value subclasses are one of a percentile in a cumulative probability distribution, a bin in a probability density function, a sample number among a series of samples, or whether its value is a statistical summary measure for the variable's scenarios, such as min, max, mean, skew, and kurtosis.