Statistical decisions under nonparametric a priori information. A program is developed for simular and experimental data handling. The main purposes are: the choice of the model most precisely describing the experiment, classification of particles and interaction processes. Procedures used: Bayes error calculation, $K$ nearest neighbour density estimation, “Leave-one-out-at-a-time” test. Used nonparametric methods provide quantitative comparison of multivariate distributions and distribution mixture classification. Applications: high energy physics, cosmic ray physics.
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References in zbMATH (referenced in 1 article , 1 standard article )
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