Accession Number : AD1014734


Title :   Statistical Inference on Memory Structure of Processes and Its Applications to Information Theory


Descriptive Note : Technical Report


Corporate Author : University of Kansas Lawrence United States


Personal Author(s) : Talata,Zsolt


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1014734.pdf


Report Date : 12 May 2016


Pagination or Media Count : 61


Abstract : Three areas were investigated. First, new memory models of discrete-time and finitely-valued information sources are introduced and a universal code for the new model class is presented. An algorithm is developed to compute the code, and its practical (polynomial) computational and storage complexities are proved. Second, a statistical method is developed to estimate the memory depth of discrete-time and continuously-valued times series from a sample. (A practical algorithm to compute the estimator is a work in progress.)


Descriptors :   STATISTICAL ANALYSIS , MATHEMATICAL MODELS , RANDOM VARIABLES , INFORMATION THEORY , probability , markov chains , algorithms , ergodic processes , polynomials , inequalities , TIME SERIES ANALYSIS


Distribution Statement : APPROVED FOR PUBLIC RELEASE