Probabilistic inference engine based on synthetic events from measured data

摘要

Automatically create abstractions of large sets of data and then probabilistic inferences based on the abstractions. The probabilistic inference is derived from the logical hierarchy using Bayesian statistics to infer a probabilistic event based upon a characteristic of the data in a hierarchy of synthetic events. The logical hierarchy of a set of a plurality of synthetic events is related by at least one characteristic of data is built by accessing a first set of data. The first set of data is organized based on a first characteristic. A second set of data different than the first set of data is accessed. A second set of data based is organized based on a second characteristic. The first characteristic and the second characteristic are processed to generate a synthetic event. The synthetic event is a third set of data representing a result of a mathematical computation defined by an operation S(p1)==F(p2).