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Information-Theoretic Methods in Data Science

Information-Theoretic Methods in Data Science
Author: Miguel R. D. Rodrigues
Publisher: Cambridge University Press
Total Pages: 561
Release: 2021-04-08
Genre: Computers
ISBN: 1108427138

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The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.


Information Theory and Statistical Learning

Information Theory and Statistical Learning
Author: Frank Emmert-Streib
Publisher: Springer Science & Business Media
Total Pages: 443
Release: 2009
Genre: Computers
ISBN: 0387848150

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This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.


Model Selection and Multimodel Inference

Model Selection and Multimodel Inference
Author: Kenneth P. Burnham
Publisher: Springer Science & Business Media
Total Pages: 512
Release: 2007-05-28
Genre: Mathematics
ISBN: 0387224564

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A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.


Information Theory, Inference and Learning Algorithms

Information Theory, Inference and Learning Algorithms
Author: David J. C. MacKay
Publisher: Cambridge University Press
Total Pages: 694
Release: 2003-09-25
Genre: Computers
ISBN: 9780521642989

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Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.


Information Theoretic Perspectives on 5G Systems and Beyond

Information Theoretic Perspectives on 5G Systems and Beyond
Author: Ivana Marić
Publisher:
Total Pages: 768
Release: 2022-06-15
Genre: Language Arts & Disciplines
ISBN: 1108271367

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Understand key information-theoretic principles that underpin the design of next-generation cellular systems with this invaluable resource. This book is the perfect tool for researchers and graduate students in the field of information theory and wireless communications, as well as for practitioners in the telecommunications industry.


Information Theory and Statistical Learning

Information Theory and Statistical Learning
Author: Frank Emmert-Streib
Publisher: Springer Science & Business Media
Total Pages: 444
Release: 2008-11-24
Genre: Computers
ISBN: 0387848169

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"Information Theory and Statistical Learning" presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines. Advance Praise for "Information Theory and Statistical Learning": "A new epoch has arrived for information sciences to integrate various disciplines such as information theory, machine learning, statistical inference, data mining, model selection etc. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places." Shun-ichi Amari, RIKEN Brain Science Institute, Professor-Emeritus at the University of Tokyo


Information Theory and Statistics

Information Theory and Statistics
Author: Solomon Kullback
Publisher: Courier Corporation
Total Pages: 436
Release: 2012-09-11
Genre: Mathematics
ISBN: 0486142043

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Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition.


Data Science and Machine Learning

Data Science and Machine Learning
Author: Dirk P. Kroese
Publisher: CRC Press
Total Pages: 538
Release: 2019-11-20
Genre: Business & Economics
ISBN: 1000730778

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Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code


Statistical Inference for Engineers and Data Scientists

Statistical Inference for Engineers and Data Scientists
Author: Pierre Moulin
Publisher: Cambridge University Press
Total Pages: 423
Release: 2019
Genre: Mathematics
ISBN: 1107185920

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A mathematically accessible textbook introducing all the tools needed to address modern inference problems in engineering and data science.


Graph Theoretic Methods in Multiagent Networks

Graph Theoretic Methods in Multiagent Networks
Author: Mehran Mesbahi
Publisher: Princeton University Press
Total Pages: 424
Release: 2010-07-01
Genre: Mathematics
ISBN: 1400835356

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This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems. The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications. The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications. This book has been adopted as a textbook at the following universities: ? University of Stuttgart, Germany Royal Institute of Technology, Sweden Johannes Kepler University, Austria Georgia Tech, USA University of Washington, USA Ohio University, USA