Performance Analysis for Sequential Statistical Discrimination of Gaussian Random Fields

Abstract

Stochastic processes stand as a versatile tool for modeling and understanding random phenomena. These processes describe system evolution while taking into account randomness and uncertainty. Nowadays, stochastic processes find application in a variety of domains, such as modeling financial markets, monitoring manufacturing procedures and predicting disease spread. This paper proposes a sequential procedure for testing hypotheses concerning the correlational structure of random fields and their trends