TY - GEN

T1 - Selected data compression

T2 - 1st International Conference Analytical and Computational Methods in Probability Theory, ACMPT 2017

AU - Suhov, Yuri

AU - Stuhl, Izabella

N1 - Funding Information:
Authors thank the Math. Department, Penn State University, for hospitality and support.

PY - 2017

Y1 - 2017

N2 - The Shannon Noiseless coding theorem (the data-compression principle) asserts that for an information source with an alphabet X= { 0, …, ℓ- 1 } and an asymptotic equipartition property, one can reduce the number of stored strings (x0, …, xn-1) ∈ Xn to ℓnh with an arbitrary small error-probability. Here h is the entropy rate of the source (calculated to the base ℓ). We consider further reduction based on the concept of utility of a string measured in terms of a rate of a weight function. The novelty of the work is that the distribution of memory is analyzed from a probabilistic point of view. A convenient tool for assessing the degree of reduction is a probabilistic large deviation principle. Assuming a Markov-type setting, we discuss some relevant formulas and examples.

AB - The Shannon Noiseless coding theorem (the data-compression principle) asserts that for an information source with an alphabet X= { 0, …, ℓ- 1 } and an asymptotic equipartition property, one can reduce the number of stored strings (x0, …, xn-1) ∈ Xn to ℓnh with an arbitrary small error-probability. Here h is the entropy rate of the source (calculated to the base ℓ). We consider further reduction based on the concept of utility of a string measured in terms of a rate of a weight function. The novelty of the work is that the distribution of memory is analyzed from a probabilistic point of view. A convenient tool for assessing the degree of reduction is a probabilistic large deviation principle. Assuming a Markov-type setting, we discuss some relevant formulas and examples.

UR - http://www.scopus.com/inward/record.url?scp=85039457663&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85039457663&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-71504-9_26

DO - 10.1007/978-3-319-71504-9_26

M3 - Conference contribution

AN - SCOPUS:85039457663

SN - 9783319715032

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 309

EP - 321

BT - Analytical and Computational Methods in Probability Theory - 1st International Conference, ACMPT 2017, Proceedings

A2 - Rykov, Vladimir V.

A2 - Singpurwalla, Nozer D.

A2 - Zubkov, Andrey M.

PB - Springer Verlag

Y2 - 23 October 2017 through 27 October 2017

ER -