on measures of entropy and information pdf

On Measures Of Entropy And Information Pdf

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Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Measures of Entropy From Data Using Infinitely Divisible Kernels Abstract: Information theory provides principled ways to analyze different inference and learning problems, such as hypothesis testing, clustering, dimensionality reduction, classification, and so forth.

How to measure the entropy of a mesoscopic system via thermoelectric transport

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Entropy is a fundamental thermodynamic quantity indicative of the accessible degrees of freedom in a system.

Rényi entropy

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Rényi entropy

Entropies quantify the diversity, uncertainty, or randomness of a system. The logarithm is conventionally taken to be base 2, especially in the context of information theory where bits are used. The collision entropy is related to the index of coincidence. In this sense, it is the strongest way to measure the information content of a discrete random variable.

Scientific production of entropy and information theory in Brazilian journals. Address for correspondence. This article is a descriptive research and a bibliometric study with a quantitative approach. Its sample is composed of 31 articles from periodic publications from different areas, such as: Accounting, Economy, Computer Sciences, Electrical and Hydraulic Engineering, Sciences, Mathematics, Physics and, also, articles that were published in an electronic library called Scientific Electronic Library Online Scielo from to Among the results, one was able to notice that the "B5" Qualis Capes classification has shown a higher number of articles, as well as that was the year with a higher number of publications.

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Entropy Measures, Maximum Entropy Principle and Emerging Applications

This book is dedicated to Prof. Kapur and his contributions to the field of entropy measures and maximum entropy applications. Eminent scholars in various fields of applied information theory have been invited to contribute to this Festschrift, collected on the occasion of his 75 th birthday. The articles cover topics in the areas of physical, biological, engineering and social sciences such as information technology, soft computing, nonlinear systems or molecular biology with a thematic coherence. The volume will be useful to researchers working in these different fields enabling them to see the underlying unity and power of entropy optimization frameworks. Skip to main content Skip to table of contents. Advertisement Hide.

In the present paper, we introduce and study Renyi's information measure entropy for residual lifetime distributions. It is shown that the proposed measure uniquely determines the distribution. We present characterizations for some lifetime models. Further, we define two new classes of life distributions based on this measure.


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Concepts and Recent Advances in Generalized Information Measures and Statistics

This book is an updated version of the information theory classic, first published in About one-third of the book is devoted to Shannon source and channel coding theorems; the remainder addresses sources, channels, and codes and on information and distortion measures and their properties. Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available. Entropy and Information Theory.

How to measure the entropy of a mesoscopic system via thermoelectric transport

The remote sensing of atmospheric or geophysical variables generally entails the indirect inference of a variable of interest x from a direct measurement of y , where the latter variable is commonly a radiance or a vector of radiances at specific wavelengths. The former variable may itself be either a scalar variable e. Peckham , citing Wiener and Feinstein , was apparently the first to invoke Shannon information theory Shannon in this context for the optimization of satellite infrared instruments for atmospheric temperature retrievals.

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