Accession Number : AD1014759


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


Descriptive Note : Technical Report,15 May 2014,14 Feb 2015


Corporate Author : University of Kansas Lawrence United States


Personal Author(s) : Talata,Zsolt


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1014759.pdf


Report Date : 12 May 2016


Pagination or Media Count : 61


Abstract : The research considered the application of context set models in information theory, and focused on constructing a universal code for this model class. 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.) Third, finitely-valued spatial processes on a d-dimensional integer lattice were considered, which are natural models of images. The open problem of statistical estimation of the spatial memory structure from a single observation of the process in a finite window has been solved.


Descriptors :   statistical analysis , time series analysis , Markov chains , information theory


Subject Categories : Statistics and Probability
      Information Science


Distribution Statement : APPROVED FOR PUBLIC RELEASE