The comparison of Bayesian and neural techniques in problems of classification to multiple categories

The determination of elemental composition of Primary Cosmic Rays in the energy range 1015–1017 eV is still an unsolved problem. Modern surface installation registering many characteristics of Extensive Air Shower (EAS) initiated in atmosphere by incident particles provide the possibility to handle data on an event-by-event basis and obtain results with reliability comparable with collider experiments. Bayesian decision making and neural approaches for data classification into multiple categories. The Parzen window method was used for multivariate density estimation along with evolutionary algorithms for net training. curacies of reconstructed proportion of different nucleus in primary flux were estimated. Both methods provide close results proving convergence to minimal achievable Bayesian risk.