Integral Observables in Problems of Indirect Detection of Hidden Patterns

A new approach to the problem of detecting hidden patterns in large-volume applied data is presented in cases where the desired pattern cannot be described directly in terms of the recorded features and is only an assumed rule for combining the effects of these quantities. A variant of the approach based on generalized precedents is presented as a multidimensional analogue of the Hough transform, when the fact of detection in the secondary distribution and the exact form of hidden patterns are estimated based on the proximity of the forecast to the actual values of only a few integral observables. Promising options for solving applied problems from these positions are proposed.