Title :
Extremal properties of likelihood-ratio quantizers
Author :
Tsitsiklis, John N.
Author_Institution :
Lab. for Inf. & Decision Syst., MIT, Cambridge, MA, USA
fDate :
4/1/1993 12:00:00 AM
Abstract :
M hypotheses and a random variable Y with a different probability distribution under each hypothesis are considered. A quantizer is applied to form a quantized random variable γ(Y ). The extreme points of the set of possible probability distributions of γ(Y), as γ ranges over all quantizers, is characterized. Optimality properties of likelihood-ratio quantizers are established for a very broad class of quantization problems, including problems involving the maximization of an Ali-Silvey (1966) distance measure and the Neyman-Pearson variant of the decentralized detection problem
Keywords :
analogue-digital conversion; probability; signal detection; decentralized detection problem; extremal properties; extreme points; likelihood-ratio quantizers; maximization; optimality properties; probability distribution; random variable; Communication system control; Geometry; Probability distribution; Quantization; Random variables; Sensor fusion;
Journal_Title :
Communications, IEEE Transactions on
