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A coherent and comprehensive theory of visual pattern classification with quantitative models, verifiable predictions and extensive empirical evidence.
Information theory always has the dual appeal of bringing important concepts to the study of communication in society, and of providing a calculus for information flows within systems. This book introduces readers to basic concepts of information theory, extending its original linear conception of communication to many variables, networks, and higher-order interactions (including loops) and developing it into a method for analyzing qualitative data. It elaborates on the algebra of entropy and information, shows how complex models of data are constructed and tested, describes algorithms for exploring multivariate structures using such models, and gives illustrative applications of these techniques. The book is designed as a text but it can also serve as a handbook for social researchers and systems theorists with an interest in communication.
Toward a Structural Theory of Action: Network Models of Social Structure, Perception, and Action centers on the concept of social structure, perceptions, and actions, as well as the strategies through which these concepts guide empirical research. This book also proposes a model of status/role-sets as patterns of relationships defining positions in the social topology. This text consists of nine chapters separated into three parts. Chapter 1 introduces the goals and organization of the book. Chapters 2-4 provide analytical synopsis of available network models of social differentiation, and then use these models in describing actual stratification. Chapter 5 presents a model in which actor interests are captured. Subsequent chapter assesses the empirical adequacy of the two predictions described in this book. Then, other chapters provide a network model of constraint and its empirical adequacy. This book will be valuable to anthropologists, economists, political scientists, and psychologists.
This book constitutes the refereed conference proceedings of the 28th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2021, held in Wrocław, Poland, in June 2021. Due to COVID-19, the conference will be held online. The 20 full papers presented in this book were carefully reviewed and selected from 48 submissions. The papers are solicited from all areas of study of local structural knowledge and global communication and computational complexities. Among the typical areas are distributed computing, communication networks, game theory, parallel computing, social networks, mobile computing
As well as providing a unified outlook on physics, Information Theory (IT) has numerous applications in chemistry and biology owing to its ability to provide a measure of the entropy/information contained within probability distributions and criteria of their information "distance" (similarity) and independence. Information Theory of Molecular Systems applies standard IT to classical problems in the theory of electronic structure and chemical reactivity. The book starts by introducing the basic concepts of modern electronic structure/reactivity theory based upon the Density Functional Theory (DFT), followed by an outline of the main ideas and techniques of IT, including several illustrative applications to molecular systems. Coverage includes information origins of the chemical bond, unbiased definition of molecular fragments, adequate entropic measures of their internal (intra-fragment) and external (inter-fragment) bond-orders and valence-numbers, descriptors of their chemical reactivity, and information criteria of their similarity and independence. Information Theory of Molecular Systems is recommended to graduate students and researchers interested in fresh ideas in the theory of electronic structure and chemical reactivity.·Provides powerful tools for tackling both classical and new problems in the theory of the molecular electronic structure and chemical reactivity·Introduces basic concepts of the modern electronic structure/reactivity theory based upon the Density Functional Theory (DFT)·Outlines main ideas and techniques of Information Theory
This book constitutes the thoroughly refereed post-conference proceedings of the 22nd International Colloquium on Structural Information and Communication Complexity, SIROCCO 2015, held in Montserrat, Spain, in July 2015. The 30 full papers presented together with 2 invited papers were carefully reviewed and selected from 78 submissions. The papers focus on the study of the interplay between communication and knowledge in multi-processor systems from both the qualitative and quantitative viewpoints.
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.
The Structural Theory of Probability addresses the interpretation of probability, often debated in the scientific community. This problem has been examined for centuries; perhaps no other mathematical calculation suffuses mankind's efforts at survival as amply as probability. In the dawn of the 20th century David Hilbert included the foundations of the probability calculus within the most vital mathematical problems; Dr. Rocchi's topical and ever-timely volume proposes a novel, exhaustive solution to this vibrant issue. Paolo Rocchi, a versatile IBM scientist, outlines a new philosophical and mathematical approach inspired by well-tested software techniques. Through the prism of computer technology he provides an innovative view on the theory of probability. Dr. Rocchi discusses in detail the mathematical tools used to clarify the meaning of probability, integrating with care numerous examples and case studies. The comprehensiveness and originality of its mathematical development make this volume an inspiring read for researchers and students alike.
Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.