Finite Memory Hypothesis Testing: Achieving Performance Bound without Randomization.

Abstract

Given a hypothesis testing problem about the bias p of a coin for heads, H sub 0: p=p sub 0 or H sub 1: p=p sub 1, and a finite memory constraint, Hellman and Cover (1970) have derived a lower bound on the error probability achievable by finite-state machines, and they have also given a procedure which achieves this performance arbitrarily closely. This procedure requires randomization, whose memory requirements sometimes one may not be able to fulfill. This paper gives a procedure in which the expedient of a laboratory processing a large number of problems is used to achieve error probabilities close to the lower bound for each problem; in essence, the procedure employs the statistics of the problems themselves in a suitable way to simulate the randomizer. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1971
Accession Number
AD0721212

Entities

People

  • B. Chandrasekaran
  • Thomas J. Harley Jr.

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Readers

  • Parallel and Distributed Computing.
  • Statistical inference.
  • Systems Analysis and Design