Method of Processing, Analyzing, and Predicting Characteristics of Radiation Fluxes by Small-Volume Samples

A method has been proposed to process and analyze the proximity by type-defining characteristics of large aggregations M > 104 of various types Q > 102 of empirical discrete random frequency vectors RFV ≡ $$\nu ( \cdot ) = {{\nu }_{0}},~...~,{{\nu }_{l}}$$ obtained from small volume samples $$10 \geqslant n = \sum\nolimits_{i = 1}^l {{{\nu }_{i}}(k = i)} $$ of random counts k = 0, 1, …, l with mean value $$\bar {k} < 5$$ over all samples. The method is based on a bijection between the RFV and its type-defining identifier $$I(\nu ,a) > 0$$ a) > 0, which is a linear statistic in the form of the scalar product of ν and the non-RFV a. The discrete multimodal empirical distributions $$C\left( {I(\nu ,a)} \right)$$ representing sequences of arranged by $$I(\nu ,a)$$ and grouped peaks facilitate the analysis and prediction of the characteristics of peaks and the RFVs forming them with low frequencies of their occurrences at the given M value.