Accession Number : AD0720810
Title : Bayesian Decision Making and Learning for Continuous-Time Markov Systems.
Descriptive Note : Interim scientific rept. no. 9,
Corporate Author : MICHIGAN STATE UNIV EAST LANSING DIV OF ENGINEERING RESEARCH
Personal Author(s) : Panayirci,Erdal ; Dubes,Richard C.
Report Date : 16 NOV 1970
Pagination or Media Count : 144
Abstract : The document is concerned with Bayesian decision making and learning algorithms for a particular problem in parametric pattern recognition in which each of a finite set of pattern classes is characterized by a continuous-time, discrete-state Markov process. The basic problem considered is that of determining rules for making decisions about the identity of the active pattern class based upon observation of a sample function in some finite interval. The stationary transition probability matrices for the processes in question are the parameters of the pattern classes. (Author)
Descriptors : (*PATTERN RECOGNITION, *DECISION THEORY), STOCHASTIC PROCESSES, STATISTICAL ANALYSIS, DECISION MAKING, DIFFERENTIAL EQUATIONS, MATRICES(MATHEMATICS), ASYMPTOTIC SERIES, THESES
Subject Categories : Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE