On Measures Of Entropy And Information Pdf
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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.
- Entropy and Information Theory
- Entropy Measures, Maximum Entropy Principle and Emerging Applications
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- How to measure the entropy of a mesoscopic system via thermoelectric transport
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.
Entropy and Information Theory
Download PDF Flyer. DOI: Since the introduction of the information measure widely known as Shannon entropy, quantifiers based on information theory and concepts such as entropic forms and statistical complexities have proven to be useful in diverse scientific research fields. This book contains introductory tutorials suitable for the general reader, together with chapters dedicated to the basic concepts of the most frequently employed information measures or quantifiers and their recent applications to different areas, including physics, biology, medicine, economics, communication and social sciences.
As these quantifiers are powerful tools for the study of general time and data series independently of their sources, this book will be useful to all those doing research connected with information analysis.
The tutorials in this volume are written at a broadly accessible level and readers will have the opportunity to acquire the knowledge necessary to use the information theory tools in their field of interest. This introductory chapter provides a basic review of the Shannon entropy and of some important related quantities like the joint entropy, the conditional entropy, the mutual information and the relative entropy. We also discuss the Fisher information, the fundamental property of concavity, the basic elements of the maximum entropy approach and the definition of entropy in the Quantum case.
We close this chapter with the axioms which determine the Shannon entropy and a brief description of other information measures. Since the introduction of the information measure widely known as Shannon entropy, quantifiers based on information theory and concepts such as entropic forms and statistical complexities have Rossignoli, Andres M.
Kowalski and Evaldo M. Curado Abstract This introductory chapter provides a basic review of the Shannon entropy and of some important related quantities like the joint entropy, the conditional entropy, the mutual information and the relative entropy.
Entropy Measures, Maximum Entropy Principle and Emerging Applications
Donate to arXiv
Download PDF Flyer. DOI: Since the introduction of the information measure widely known as Shannon entropy, quantifiers based on information theory and concepts such as entropic forms and statistical complexities have proven to be useful in diverse scientific research fields.
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. The so-called Shannon information content SIC of an indirect measurement is defined by the resulting reduction in the Shannon entropy of the probability density function PDF of the variable of interest.
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How to measure the entropy of a mesoscopic system via thermoelectric transport
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.
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