Title :
Application of learning algorithms to hypotheses testing problems
Author :
Thathacher, M.A.L. ; Varahan, S.
Author_Institution :
Yale University, Connecticut, USA
Abstract :
Described in this paper is an application of variable structure stochastic automata to the solution of the hypothesis testing problem. Given the upper bounds on the error probabilities of the two kinds a design procedure for devising an algorithm for the stochastic automaton which ensures a proper decision is developed. The method is illustrated by an application to a simple detection problem of a known constant signal in additive gaussian noise.
Keywords :
Additive noise; Algorithm design and analysis; Automatic testing; Gaussian noise; Learning automata; Signal synthesis; Stochastic processes; Upper bound;
Conference_Titel :
Decision and Control including the 12th Symposium on Adaptive Processes, 1973 IEEE Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/CDC.1973.269159
