Joint String Complexity for Markov Sources: Small Data Matters

TL;DR

This paper investigates the joint string complexity for strings generated by Markov sources, revealing linear growth for similar sources and sublinear growth for different sources, using advanced analytic techniques.

Contribution

It introduces the concept of joint string complexity for Markov sources and provides a detailed asymptotic analysis revealing growth behaviors based on source similarity.

Findings

Joint string complexity grows linearly for statistically indistinguishable sources.

Joint string complexity grows sublinearly when sources are statistically different.

Application of advanced analytic methods uncovers oscillatory phenomena in complexity analysis.

Abstract

String complexity is defined as the cardinality of a set of all distinct words (factors) of a given string. For two strings, we introduce the joint string complexity as the cardinality of a set of words that are common to both strings. String complexity finds a number of applications from capturing the richness of a language to finding similarities between two genome sequences. In this paper we analyze the joint string complexity when both strings are generated by Markov sources. We prove that the joint string complexity grows linearly (in terms of the string lengths) when both sources are statistically indistinguishable and sublinearly when sources are statistically not the same. Precise analysis of the joint string complexity turns out to be quite challenging requiring subtle singularity analysis and saddle point method over infinity many saddle points leading to novel oscillatory phenomena with single and double periodicities. To overcome these challenges, we apply powerful analytic techniques such as multivariate generating functions, multivariate depoissonization and Mellin transform, spectral matrix analysis, and complex asymptotic methods.