Sequence Alignment Algorithm for Statistical Similarity Assessment

This paper presents a new approach to statistical similarity assessment based on sequence alignment. The algorithm performs mutual matching of two random sequences by successively searching for common elements and applying breaks matchless in the function exponential cost. As result, varying significantly generate high-cost alignment, while low-cost introduced interruptions allow inferring nature dependence. most important advantage is an easy interpretation obtained results parameters: stretch ratio operation method has been simulation tested verified with use real data from hardware number generators. proposed solution ensures simple implementation enabling integration solutions, only any length predisposes online testing.