Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informed decisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated. In this pioneering book, Amos Golan, a leader in info-metrics, focuses on unifying information processing, modeling and inference within a single constrained optimization framework. Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, and progresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains numerous multidisciplinary applications and case studies, which demonstrate the simplicity and generality of the framework in real world settings. Examples include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, strategy inference for interacting entities, incorporation of prior information, option pricing, and modeling an interacting social system. Graphical representations illustrate how results can be visualized while exercises and problem sets facilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.

The Age of Information is destined to become an important research topic in networked systems. This monograph provides the reader with an easy-to-read tutorial-like introduction into this novel approach of dealing with the freshness of information within systems.

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This book provides the reader with the mathematical framework required to fully explore the potential of small quantum information processing devices. As decoherence will continue to limit their size, it is essential to master the conceptual tools which make such investigations possible. A strong emphasis is given to information measures that are essential for the study of devices of finite size, including Rényi entropies and smooth entropies. The presentation is self-contained and includes rigorous and concise proofs of the most important properties of these measures. The first chapters will introduce the formalism of quantum mechanics, with particular emphasis on norms and metrics for quantum states. This is necessary to explore quantum generalizations of Rényi divergence and conditional entropy, information measures that lie at the core of information theory. The smooth entropy framework is discussed next and provides a natural means to lift many arguments from information theory to the quantum setting. Finally selected applications of the theory to statistics and cryptography are discussed. The book is aimed at graduate students in Physics and Information Theory. Mathematical fluency is necessary, but no prior knowledge of quantum theory is required.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Provides an introduction to modern statistical theory for social and health scientists while invoking minimal modeling assumptions.

In this new edition of Foundations for Moral Relativism a distinguished moral philosopher tames a bugbear of current debate about cultural difference. J. David Velleman shows that different communities can indeed be subject to incompatible moralities, because their local mores are rationally binding. At the same time, he explains why the mores of different communities, even when incompatible, are still variations on the same moral themes. The book thus maps out a universe of many moral worlds without, as Velleman puts it, "moral black holes”. The six self-standing chapters discuss such diverse topics as online avatars and virtual worlds, lying in Russian and truth-telling in Quechua, the pleasure of solitude and the fear of absurdity. Accessibly written, this book presupposes no prior training in philosophy.

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.

Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In Advances in Info-Metrics, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah bring together a group of thirty experts to expand the study of info-metrics across the sciences and demonstrate how to solve problems using this interdisciplinary framework. Building on the theoretical underpinnings of info-metrics, the volume sheds new light on statistical inference, information, and general problem solving. The book explores the basis of information-theoretic inference and its mathematical and philosophical foundations. It emphasizes the interrelationship between information and inference and includes explanations of model building, theory creation, estimation, prediction, and decision making. Each of the nineteen chapters provides the necessary tools for using the info-metrics framework to solve a problem. The collection covers recent developments in the field, as well as many new cross-disciplinary case studies and examples. Designed to be accessible for researchers, graduate students, and practitioners across disciplines, this book provides a clear, hands-on experience for readers interested in solving problems when presented with incomplete and imperfect information.

This text enables IT managers to create a detailed and practical SLM strategy and shows them how to implement it in their organizations.