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
      Cybernetics
      Bionics

Distribution Statement : APPROVED FOR PUBLIC RELEASE