GENERALIZATION OF BAYESIAN RULE OF MANY SIMPLE HYPOTHESES TESTING

K. J. Kachiashvili ()
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K. J. Kachiashvili: The I. Vekua Institute of Applied Mathematics of the Tbilisi State University, 2, st. University, Tbilisi, 380043, Georgia

International Journal of Information Technology & Decision Making (IJITDM), 2003, vol. 02, issue 01, 41-70

Abstract: There are different methods of statistical hypotheses testing.1–4Among them, is Bayesian approach. A generalization of Bayesian rule of many hypotheses testing is given below. It consists of decision rule dimensionality with respect to the number of tested hypotheses, which allows to make decisions more differentiated than in the classical case and to state, instead of unconstrained optimization problem, constrained one that enables to make guaranteed decisions concerning errors of true decisions rejection, which is the key point when solving a number of practical problems. These generalizations are given both for a set of simple hypotheses, each containing one space point, and hypotheses containing a finite set of separated space points.

Keywords: Bayesian rule; hypotheses testing; decision function; stepwise loss function; risk function (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1142/S0219622003000525

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