Posterior detection of a given number of identical subsequences in a quasi-periodic sequence (Q1395276)

This paper is devoted to the problem of detection of a given number of identical subsequences in a quasi-periodic sequence distorted by uncorrelated Gaussian noise with a known variance. A posterior computational algorithm for this problem is justified. This problem is shown to be a specific hypothesis-testing problem for the mean of a Gaussian random vector. Recurrence formulas based on maximum likelihood are obtained for stepwise discrete optimization.