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Information Theoretic Methods In Data Science
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Book Synopsis Information-Theoretic Methods in Data Science by : Miguel R. D. Rodrigues
Download or read book Information-Theoretic Methods in Data Science written by Miguel R. D. Rodrigues and published by Cambridge University Press. This book was released on 2021-04-08 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Information Theory and Statistical Learning by : Frank Emmert-Streib
Download or read book Information Theory and Statistical Learning written by Frank Emmert-Streib and published by Springer Science & Business Media. This book was released on 2009 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Model Selection and Multimodel Inference by : Kenneth P. Burnham
Download or read book Model Selection and Multimodel Inference written by Kenneth P. Burnham and published by Springer Science & Business Media. This book was released on 2007-05-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Information Theoretic Perspectives on 5G Systems and Beyond by : Ivana Marić
Download or read book Information Theoretic Perspectives on 5G Systems and Beyond written by Ivana Marić and published by . This book was released on 2022-06-15 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese
Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Information Theoretic Learning by : Jose C. Principe
Download or read book Information Theoretic Learning written by Jose C. Principe and published by Springer Science & Business Media. This book was released on 2010-04-06 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Information-Theoretic Methods for Estimating of Complicated Probability Distributions by : Zhi Zong
Download or read book Information-Theoretic Methods for Estimating of Complicated Probability Distributions written by Zhi Zong and published by Elsevier. This book was released on 2006-08-15 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixing up various disciplines frequently produces something that are profound and far-reaching. Cybernetics is such an often-quoted example. Mix of information theory, statistics and computing technology proves to be very useful, which leads to the recent development of information-theory based methods for estimating complicated probability distributions. Estimating probability distribution of a random variable is the fundamental task for quite some fields besides statistics, such as reliability, probabilistic risk analysis (PSA), machine learning, pattern recognization, image processing, neural networks and quality control. Simple distribution forms such as Gaussian, exponential or Weibull distributions are often employed to represent the distributions of the random variables under consideration, as we are taught in universities. In engineering, physical and social science applications, however, the distributions of many random variables or random vectors are so complicated that they do not fit the simple distribution forms at al. Exact estimation of the probability distribution of a random variable is very important. Take stock market prediction for example. Gaussian distribution is often used to model the fluctuations of stock prices. If such fluctuations are not normally distributed, and we use the normal distribution to represent them, how could we expect our prediction of stock market is correct? Another case well exemplifying the necessity of exact estimation of probability distributions is reliability engineering. Failure of exact estimation of the probability distributions under consideration may lead to disastrous designs. There have been constant efforts to find appropriate methods to determine complicated distributions based on random samples, but this topic has never been systematically discussed in detail in a book or monograph. The present book is intended to fill the gap and documents the latest research in this subject. Determining a complicated distribution is not simply a multiple of the workload we use to determine a simple distribution, but it turns out to be a much harder task. Two important mathematical tools, function approximation and information theory, that are beyond traditional mathematical statistics, are often used. Several methods constructed based on the two mathematical tools for distribution estimation are detailed in this book. These methods have been applied by the author for several years to many cases. They are superior in the following senses: (1) No prior information of the distribution form to be determined is necessary. It can be determined automatically from the sample; (2) The sample size may be large or small; (3) They are particularly suitable for computers. It is the rapid development of computing technology that makes it possible for fast estimation of complicated distributions. The methods provided herein well demonstrate the significant cross influences between information theory and statistics, and showcase the fallacies of traditional statistics that, however, can be overcome by information theory. Key Features: - Density functions automatically determined from samples - Free of assuming density forms - Computation-effective methods suitable for PC- density functions automatically determined from samples- Free of assuming density forms- Computation-effective methods suitable for PC
Download or read book Information Theory written by JV Stone and published by Sebtel Press. This book was released on 2015-01-01 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally developed by Claude Shannon in the 1940s, information theory laid the foundations for the digital revolution, and is now an essential tool in telecommunications, genetics, linguistics, brain sciences, and deep space communication. In this richly illustrated book, accessible examples are used to introduce information theory in terms of everyday games like ‘20 questions’ before more advanced topics are explored. Online MatLab and Python computer programs provide hands-on experience of information theory in action, and PowerPoint slides give support for teaching. Written in an informal style, with a comprehensive glossary and tutorial appendices, this text is an ideal primer for novices who wish to learn the essential principles and applications of information theory.
Book Synopsis Graph Theoretic Methods in Multiagent Networks by : Mehran Mesbahi
Download or read book Graph Theoretic Methods in Multiagent Networks written by Mehran Mesbahi and published by Princeton University Press. This book was released on 2010-07-01 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Methodologies and Applications of Computational Statistics for Machine Intelligence by : Samanta, Debabrata
Download or read book Methodologies and Applications of Computational Statistics for Machine Intelligence written by Samanta, Debabrata and published by IGI Global. This book was released on 2021-06-25 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians.
Book Synopsis Augmented Cognition. Human Cognition and Behavior by : Dylan D. Schmorrow
Download or read book Augmented Cognition. Human Cognition and Behavior written by Dylan D. Schmorrow and published by Springer Nature. This book was released on 2020-07-10 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of 14th International Conference on Augmented Cognition, AC 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020, in July 2020. The conference was planned to be held in Copenhagen, Denmark, but had to change to a virtual conference mode due to the COVID-19 pandemic. From a total of 6326 submissions, a total of 1439 papers and 238 posters has been accepted for publication in the HCII 2020 proceedings. The 21 papers presented in this volume were organized in topical sections as follows: cognitive modeling, perception, emotion and interaction; electroencephalography and BCI; and AI and augmented cognition.
Book Synopsis Women in Telecommunications by : Maria Sabrina Greco
Download or read book Women in Telecommunications written by Maria Sabrina Greco and published by Springer Nature. This book was released on 2023-11-06 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a breadth of innovative and impactful research in the field of telecommunications led by women investigators. Topics covered include satellite communications, cognitive radars, remote sensing sensor networks, quantum Internet, and cyberspace. These topics touch on many of the challenges facing the world today and these solutions by women researchers are valuable for their technical excellence and their non-traditional perspective. As an important part of the Women in Engineering and Science book series, the work highlights the contribution of women leaders in telecommunications, inspiring women and men, girls and boys to enter and apply themselves to secure our future in.
Book Synopsis Set-Theoretic Methods for the Social Sciences by : Carsten Q. Schneider
Download or read book Set-Theoretic Methods for the Social Sciences written by Carsten Q. Schneider and published by Cambridge University Press. This book was released on 2012-08-30 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Qualitative Comparative Analysis (QCA) and other set-theoretic methods distinguish themselves from other approaches to the study of social phenomena by using sets and the search for set relations. In virtually all social science fields, statements about social phenomena can be framed in terms of set relations, and using set-theoretic methods to investigate these statements is therefore highly valuable. This book guides readers through the basic principles of set theory and then on to the applied practices of QCA. It provides a thorough understanding of basic and advanced issues in set-theoretic methods together with tricks of the trade, software handling and exercises. Most arguments are introduced using examples from existing research. The use of QCA is increasing rapidly and the application of set-theory is both fruitful and still widely misunderstood in current empirical comparative social research. This book provides the comprehensive guide to these methods for researchers across the social sciences.
Book Synopsis Soft Computing in Data Science by : Michael W. Berry
Download or read book Soft Computing in Data Science written by Michael W. Berry and published by Springer. This book was released on 2016-09-17 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics.
Book Synopsis Deep Learning for Biomedical Image Reconstruction by : Jong Chul Ye
Download or read book Deep Learning for Biomedical Image Reconstruction written by Jong Chul Ye and published by Cambridge University Press. This book was released on 2023-09-30 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.
Book Synopsis Dimensionality Reduction in Data Science by : Max Garzon
Download or read book Dimensionality Reduction in Data Science written by Max Garzon and published by Springer Nature. This book was released on 2022-07-28 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated. The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains. This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting. This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.
Book Synopsis Information Theoretic Principles for Agent Learning by : Jerry D. Gibson
Download or read book Information Theoretic Principles for Agent Learning written by Jerry D. Gibson and published by Springer Nature. This book was released on with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt:
