scholarly journals Some Inequalities in Information Theory Using Tsallis Entropy

2018 ◽  
Vol 2018 ◽  
pp. 1-4
Author(s):  
Litegebe Wondie ◽  
Satish Kumar
Keyword(s):  
Information Theory ◽  
Coding Theory ◽  
Tsallis Entropy ◽  
Information Measure ◽  
Code Length ◽  
Generalized Information

We presenta relation betweenTsallis’s entropy and generalizedKerridge inaccuracywhich is called generalizedShannon inequalityand is well-known generalization ininformation theoryand then give its application in coding theory. The objective of the paper is to establish a result on noiseless coding theorem for the proposed mean code length in terms of generalized information measure of orderξ.

The branching property in generalized information theory

1978 ◽  
Vol 10 (04) ◽  
pp. 788-802
Author(s):  
Bruce Ebanks
Keyword(s):  
Information Theory ◽  
Probability Space ◽  
Binary Operation ◽  
Information Measure ◽  
Additive Functions ◽  
Expected Information ◽  
Generalized Information ◽  
Generalized Information Theory ◽  
Branching Property

It is shown that every measure of expected information which has the branching property is of the form where J is a given information measure which is compositive under a regular binary operation and the Ψ n are antisymmetric, bi-additive functions. In a probability space, such measures (entropies) take the form

The branching property in generalized information theory

1978 ◽  
Vol 10 (4) ◽  
pp. 788-802 ◽  
Author(s):  
Bruce Ebanks
Keyword(s):  
Information Theory ◽  
Probability Space ◽  
Binary Operation ◽  
Information Measure ◽  
Additive Functions ◽  
Expected Information ◽  
Generalized Information ◽  
Generalized Information Theory ◽  
Branching Property

It is shown that every measure of expected information which has the branching property is of the form where J is a given information measure which is compositive under a regular binary operation and the Ψn are antisymmetric, bi-additive functions. In a probability space, such measures (entropies) take the form

scholarly journals A permutation information theory tour through different interest rate maturities: the Libor case

2015 ◽  
Vol 373 (2056) ◽  
pp. 20150119 ◽  
Author(s):  
Aurelio Fernández Bariviera ◽  
María Belén Guercio ◽  
Lisana B. Martinez ◽  
Osvaldo A. Rosso
Keyword(s):  
Information Theory ◽  
Interest Rate ◽  
Interest Rates ◽  
Fisher Information ◽  
Shannon Entropy ◽  
Anomalous Behaviour ◽  
Information Measure ◽  
Special Mechanism ◽  
Fisher Information Measure ◽  
Japanese Yen

This paper analyses Libor interest rates for seven different maturities and referred to operations in British pounds, euros, Swiss francs and Japanese yen, during the period 2001–2015. The analysis is performed by means of two quantifiers derived from information theory: the permutation Shannon entropy and the permutation Fisher information measure. An anomalous behaviour in the Libor is detected in all currencies except euros during the years 2006–2012. The stochastic switch is more severe in one, two and three months maturities. Given the special mechanism of Libor setting, we conjecture that the behaviour could have been produced by the manipulation that was uncovered by financial authorities. We argue that our methodology is pertinent as a market overseeing instrument.

scholarly journals A Novel Encoding Algorithm for Textual Data Compression

2020 ◽  
Author(s):  
Anas Al-okaily ◽  
Abdelghani Tbakhi
Keyword(s):  
Information Theory ◽  
Data Compression ◽  
Computer Science ◽  
Coding Theory ◽  
Fundamental Problem ◽  
Novel Approach ◽  
Textual Data ◽  
Science Information

ABSTRACTData compression is a fundamental problem in the fields of computer science, information theory, and coding theory. The need for compressing data is to reduce the size of the data so that the storage and the transmission of them become more efficient. Motivated from resolving the compression of DNA data, we introduce a novel encoding algorithm that works for any textual data including DNA data. Moreover, the design of this algorithm paves a novel approach so that researchers can build up on and resolve better the compression problem of DNA or textual data.

scholarly journals A NEW GENERALIZED VARENTROPY AND ITS PROPERTIES

2020 ◽  
Vol 6 (1) ◽  
pp. 114
Author(s):  
Saeid Maadani ◽  
Gholam Reza Mohtashami Borzadaran ◽  
Abdol Hamid Rezaei Roknabadi
Keyword(s):  
Information Theory ◽  
Order Statistics ◽  
Information Content ◽  
Random Variable ◽  
Tsallis Entropy ◽  
Shannon Information ◽  
Lifetime Distributions

The variance of Shannon information related to the random variable \(X\), which is called varentropy, is a measurement that indicates, how the information content of \(X\) is scattered around its entropy and explains its various applications in information theory, computer sciences, and statistics. In this paper, we introduce a new generalized varentropy based on the Tsallis entropy and also obtain some results and bounds for it. We compare the varentropy with the Tsallis varentropy. Moreover, we explain the Tsallis varentropy of the order statistics and analyse this concept in residual (past) lifetime distributions and then introduce two new classes of distributions by them.

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