Entropy Measures For Data Analysis Theory Algorithms And Applications
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Entropy Measures For Data Analysis
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Author : Karsten Keller
language : en
Publisher: MDPI
Release Date : 2019-12-19
Entropy Measures For Data Analysis written by Karsten Keller and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-19 with Science categories.
Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.
Entropy Measures For Data Analysis Theory Algorithms And Applications
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Author : Karsten Keller
language : en
Publisher:
Release Date : 2019
Entropy Measures For Data Analysis Theory Algorithms And Applications written by Karsten Keller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Engineering (General). Civil engineering (General) categories.
Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.
Entropy Measures Maximum Entropy Principle And Emerging Applications
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Author : Karmeshu
language : en
Publisher: Springer
Release Date : 2012-10-01
Entropy Measures Maximum Entropy Principle And Emerging Applications written by Karmeshu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-01 with Technology & Engineering categories.
The last two decades have witnessed an enormous growth with regard to ap plications of information theoretic framework in areas of physical, biological, engineering and even social sciences. In particular, growth has been spectac ular in the field of information technology,soft computing,nonlinear systems and molecular biology. Claude Shannon in 1948 laid the foundation of the field of information theory in the context of communication theory. It is in deed remarkable that his framework is as relevant today as was when he 1 proposed it. Shannon died on Feb 24, 2001. Arun Netravali observes "As if assuming that inexpensive, high-speed processing would come to pass, Shan non figured out the upper limits on communication rates. First in telephone channels, then in optical communications, and now in wireless, Shannon has had the utmost value in defining the engineering limits we face". Shannon introduced the concept of entropy. The notable feature of the entropy frame work is that it enables quantification of uncertainty present in a system. In many realistic situations one is confronted only with partial or incomplete information in the form of moment, or bounds on these values etc. ; and it is then required to construct a probabilistic model from this partial information. In such situations, the principle of maximum entropy provides a rational ba sis for constructing a probabilistic model. It is thus necessary and important to keep track of advances in the applications of maximum entropy principle to ever expanding areas of knowledge.
Universal Estimation Of Information Measures For Analog Sources
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Author : Qing Wang
language : en
Publisher: Now Publishers Inc
Release Date : 2009-05-26
Universal Estimation Of Information Measures For Analog Sources written by Qing Wang and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-26 with Computers categories.
Entropy, mutual information and divergence measure the randomness, dependence and dissimilarity, respectively, of random objects. In addition to their prominent role in information theory, they have found numerous applications, among others, in probability theory statistics, physics, chemistry, molecular biology, ecology, bioinformatics, neuroscience, machine learning, linguistics, and finance. Many of these applications require a universal estimate of information measures which does not assume knowledge of the statistical properties of the observed data. Over the past few decades, several nonparametric algorithms have been proposed to estimate information measures. Universal Estimation of Information Measures for Analog Sources presents a comprehensive survey of universal estimation of information measures for memoryless analog (real-valued or real vector-valued) sources with an emphasis on the estimation of mutual information and divergence and their applications. The book reviews the consistency of the universal algorithms and the corresponding sufficient conditions as well as their speed of convergence. Universal Estimation of Information Measures for Analog Sources provides a comprehensive review of an increasingly important topic in Information Theory. It will be of interest to students, practitioners and researchers working in Information Theory
Information Theoretic Learning
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Author : Jose C. Principe
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-06
Information Theoretic Learning written by Jose C. Principe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-06 with Computers categories.
This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.
Proceedings Of The 4th International Conference On Electronics Biomedical Engineering And Health Informatics
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Author : Triwiyanto Triwiyanto
language : en
Publisher: Springer Nature
Release Date :
Proceedings Of The 4th International Conference On Electronics Biomedical Engineering And Health Informatics written by Triwiyanto Triwiyanto and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Neutrosophic Entropy Measures For The Normal Distribution Theory And Applications
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Author : Rehan Ahmad Khan Sherwani
language : en
Publisher: Infinite Study
Release Date :
Neutrosophic Entropy Measures For The Normal Distribution Theory And Applications written by Rehan Ahmad Khan Sherwani and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Entropy is a measure of uncertainty and often used in information theory to determine the precise testimonials about unclear situations. Different entropy measures available in the literature are based on the exact form of the observations and lacks in dealing with the interval-valued data. The interval-valued data often arises from the situations having ambiguity, imprecise, unclear, indefinite, or vague states of the experiment and is called neutrosophic data. In this research modified forms of different entropy measures for normal probability distribution have been proposed by considering the neutrosophic form data. The performance of the proposed neutrosophic entropies for normal distribution has been assessed via a simulation study. Moreover, the proposed measures are also applied to two real data sets for their wide applicability.
Maximum Entropy And Bayesian Methods
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Author : G. Erickson
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Maximum Entropy And Bayesian Methods written by G. Erickson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 -8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (point-spread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people.
Maximum Entropy And Bayesian Methods Garching Germany 1998
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Author : Wolfgang von der Linden
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Maximum Entropy And Bayesian Methods Garching Germany 1998 written by Wolfgang von der Linden and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
In 1978 Edwin T. Jaynes and Myron Tribus initiated a series of workshops to exchange ideas and recent developments in technical aspects and applications of Bayesian probability theory. The first workshop was held at the University of Wyoming in 1981 organized by C.R. Smith and W.T. Grandy. Due to its success, the workshop was held annually during the last 18 years. Over the years, the emphasis of the workshop shifted gradually from fundamental concepts of Bayesian probability theory to increasingly realistic and challenging applications. The 18th international workshop on Maximum Entropy and Bayesian Methods was held in Garching / Munich (Germany) (27-31. July 1998). Opening lectures by G. Larry Bretthorst and by Myron Tribus were dedicated to one of th the pioneers of Bayesian probability theory who died on the 30 of April 1998: Edwin Thompson Jaynes. Jaynes revealed and advocated the correct meaning of 'probability' as the state of knowledge rather than a physical property. This inter pretation allowed him to unravel longstanding mysteries and paradoxes. Bayesian probability theory, "the logic of science" - as E.T. Jaynes called it - provides the framework to make the best possible scientific inference given all available exper imental and theoretical information. We gratefully acknowledge the efforts of Tribus and Bretthorst in commemorating the outstanding contributions of E.T. Jaynes to the development of probability theory.
An Introduction To Transfer Entropy
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Author : Terry Bossomaier
language : en
Publisher: Springer
Release Date : 2016-11-15
An Introduction To Transfer Entropy written by Terry Bossomaier and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-15 with Computers categories.
This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth. A key feature of the approach is the authors' work to show the relationship between information flow and complexity. The later chapters demonstrate information transfer in canonical systems, and applications, for example in neuroscience and in finance. The book will be of value to advanced undergraduate and graduate students and researchers in the areas of computer science, neuroscience, physics, and engineering.