Information Theoretic Models And Applications
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| Author | : Kenneth P. Burnham |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 512 |
| Release | : 2007-05-28 |
| Genre | : Mathematics |
| ISBN | : 0387224564 |
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.
| Author | : Miguel R. D. Rodrigues |
| Publisher | : Cambridge University Press |
| Total Pages | : 561 |
| Release | : 2021-04-08 |
| Genre | : Computers |
| ISBN | : 1108427138 |
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.
| Author | : Kenneth P. Burnham |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 373 |
| Release | : 2013-11-11 |
| Genre | : Mathematics |
| ISBN | : 1475729170 |
Statisticians and applied scientists must often select a model to fit empirical data. This book discusses the philosophy and strategy of selecting such a model using the information theory approach pioneered by Hirotugu Akaike. This approach focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. The book includes practical applications in biology and environmental science.
| Author | : Rafael F. Schaefer |
| Publisher | : Cambridge University Press |
| Total Pages | : 581 |
| Release | : 2017-06-16 |
| Genre | : Computers |
| ISBN | : 1107132266 |
Learn how information theoretic approaches can inform the design of more secure information systems and networks with this expert guide. Covering theoretical models, analytical results, and the state of the art in research, it will be of interest to researchers, graduate students, and practitioners working in communications engineering.
| Author | : Matthias Dehmer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 409 |
| Release | : 2011-08-26 |
| Genre | : Mathematics |
| ISBN | : 0817649042 |
For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. As such, it marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines and can serve as a valuable resource for a diverse audience of advanced students and professional scientists. While it is primarily intended as a reference for research, the book could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.
| Author | : Mukesh Sarangal |
| Publisher | : Sudwestdeutscher Verlag Fur Hochschulschriften AG |
| Total Pages | : 176 |
| Release | : 2016-12-19 |
| Genre | : |
| ISBN | : 9783838153520 |
The present book deals with the quantitative measures of information so as to provide their applications in different fields of Operations Research and Statistics. It is worth mentioning that the two basic concepts, viz, entropy and divergence which are closely related to each other have been investigated and applied to various disciplines of Mathematical Sciences. Another idea providing a holistic view of problems comes under the domain of Jaynes "Maximum Entropy Principle" which deals with the problems of obtaining the most unbiased probability distributions under a set of specified constraints. The contents of the book provide a detailed study of the maximum entropy principle. I sincerely hope that the present volume of the book will be useful to all those interested in the mathematical models and their optimization. Moreover, it will be a source of inspiration and encouragement to all research scholars and teachers to discourse the subject for the discovery of new insights.
| Author | : Shlomo Shamai (Shitz) |
| Publisher | : MDPI |
| Total Pages | : 294 |
| Release | : 2021-01-13 |
| Genre | : Technology & Engineering |
| ISBN | : 3039438174 |
Modern, current, and future communications/processing aspects motivate basic information-theoretic research for a wide variety of systems for which we do not have the ultimate theoretical solutions (for example, a variety of problems in network information theory as the broadcast/interference and relay channels, which mostly remain unsolved in terms of determining capacity regions and the like). Technologies such as 5/6G cellular communications, Internet of Things (IoT), and mobile edge networks, among others, not only require reliable rates of information measured by the relevant capacity and capacity regions, but are also subject to issues such as latency vs. reliability, availability of system state information, priority of information, secrecy demands, energy consumption per mobile equipment, sharing of communications resources (time/frequency/space), etc. This book, composed of a collection of papers that have appeared in the Special Issue of the Entropy journal dedicated to “Information Theory for Data Communications and Processing”, reflects, in its eleven chapters, novel contributions based on the firm basic grounds of information theory. The book chapters address timely theoretical and practical aspects that constitute both interesting and relevant theoretical contributions, as well as direct implications for modern current and future communications systems.
| Author | : Robert B. Ash |
| Publisher | : Halsted Press |
| Total Pages | : 360 |
| Release | : 1965 |
| Genre | : Computers |
| ISBN | : |
| Author | : Sucar, L. Enrique |
| Publisher | : IGI Global |
| Total Pages | : 444 |
| Release | : 2011-10-31 |
| Genre | : Computers |
| ISBN | : 160960167X |
One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.
| Author | : Christopher D. Manning |
| Publisher | : Cambridge University Press |
| Total Pages | : |
| Release | : 2008-07-07 |
| Genre | : Computers |
| ISBN | : 1139472100 |
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
