This text gives students a clear and easily understood introduction to entropy - a central concept in thermodynamics, but one which is often regarded as the most difficult to grasp. Professor Dugdale first presents a classical and historical view of entropy, looking in detail at the scientists who developed the concept, and at how they arrived at their ideas. This is followed by a statistical treatment which provides a more physical portrait of entropy, relating it to disorder and showing how physical and chemical systems tend to states of order at low temperatures. Dugdale includes here a brief account of some of the more intriguing manifestations of order in properties such as superconductivity and superfluidity.Entropy and Its Physical Meaning also includes a number of exercises which can be used for both self- learning and class work. It is intended to provide a complete understanding of the concept of entropy, making it valuable reading for undergraduates in physics, physical sciences and engineering, and for students studying thermodynamics within other science courses such as meteorology, biology and medicine.

This book is an updated version of the information theory classic, first published in 1990. About one-third of the book is devoted to Shannon source and channel coding theorems; the remainder addresses sources, channels, and codes and on information and distortion measures and their properties. New in this edition: Expanded treatment of stationary or sliding-block codes and their relations to traditional block codes Expanded discussion of results from ergodic theory relevant to information theory Expanded treatment of B-processes -- processes formed by stationary coding memoryless sources New material on trading off information and distortion, including the Marton inequality New material on the properties of optimal and asymptotically optimal source codes New material on the relationships of source coding and rate-constrained simulation or modeling of random processes Significant material not covered in other information theory texts includes stationary/sliding-block codes, a geometric view of information theory provided by process distance measures, and general Shannon coding theorems for asymptotic mean stationary sources, which may be neither ergodic nor stationary, and d-bar continuous channels.

This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, based on maximum likelihood estimators, as well as Wald’s statistics, likelihood ratio statistics and Rao’s score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically, this book presents a robust version of the classical Wald statistical test, for testing simple and composite null hypotheses for general parametric models, based on minimum divergence estimators.

The concept of entropy in thermodynamics is a complex one, though it is fundamental in understanding physics, the workings of the mind, and biology. Entropy is the measure of the quality of energy, and it can also refer to the turn from order to disorder or randomness in isolated systems. In open systems, such as biology, entropy is formulated in terms of production and energy flow. This book establishes a novel view of complex biological systems and the earth using this concept of entropy, encompassing the interdisciplinary area of biology, ecology and physics. This book considers the development over time of a range of biologically complex systems such as plants, animals, humans, and ecosystems, describing them in terms of the second law of thermodynamics, entropy. With its broad coverage of biological systems, this book will be useful for students of environmental science as well as students in biology and physics. - Includes discussion of multiple complex systems including the earth and biological systems within it. - Suitable for those with little physics background who wish to learn how the laws of physics apply to ecological systems. - Clearly organized by system, making information easy to access.

This is the first book on spatial entropy in the scientific literature. It links spatial entropy with landscape analysis, landscape diversity and geo-information. It gives all the essential tools that a researcher needs in order to study the spatial entropy of physical as well as artificial landscapes (created with artificial life, swarm intelligence etc). This book explores the fascinating world of the interplay between spatial entropy, spatial information, self-organization and emergence and gives geographers and landscape scientists several alternative mathematical methods to study them, i.e. Shannon's formula, measures from non-extensive thermodynamics, from directional statistics and network theory. An essential book for researchers in landscape analysis and geo-informatics.

This volume represents the proceedings of the Ninth Annual MaxEnt Workshop, held at Dartmouth College in Hanover, New Hampshire, on August 14-18, 1989. These annual meetings are devoted to the theory and practice of Bayesian Probability and the Maximum Entropy Formalism. The fields of application exemplified at MaxEnt '89 are as diverse as the foundations of probability theory and atmospheric carbon variations, the 1987 Supernova and fundamental quantum mechanics. Subjects include sea floor drug absorption in man, pressures, neutron scattering, plasma equilibrium, nuclear magnetic resonance, radar and astrophysical image reconstruction, mass spectrometry, generalized parameter estimation, delay estimation, pattern recognition, heave responses in underwater sound and many others. The first ten papers are on probability theory, and are grouped together beginning with the most abstract followed by those on applications. The tenth paper involves both Bayesian and MaxEnt methods and serves as a bridge to the remaining papers which are devoted to Maximum Entropy theory and practice. Once again, an attempt has been made to start with the more theoretical papers and to follow them with more and more practical applications. Papers number 29, 30 and 31, by Kesaven, Seth and Kapur, represent a somewhat different, perhaps even "unorthodox" viewpoint, and are included here even though the editor and, indeed many in the audience at Dartmouth, disagreed with their content. I feel that scientific disagreements are essential in any developing field, and often lead to a deeper understanding.

This latest addition to the successful Network Biology series presents current methods for determining the entropy of networks, making it the first to cover the recently established Quantitative Graph Theory. An excellent international team of editors and contributors provides an up-to-date outlook for the field, covering a broad range of graph entropy-related concepts and methods. The topics range from analyzing mathematical properties of methods right up to applying them in real-life areas. Filling a gap in the contemporary literature this is an invaluable reference for a number of disciplines, including mathematicians, computer scientists, computational biologists, and structural chemists.

This ground-breaking Handbook presents a state-of-the-art exploration of entropy, complexity and spatial dynamics from fundamental theoretical, empirical and methodological perspectives. It considers how foundational theories can contribute to new advances, including novel modeling and empirical insights at different sectoral, spatial and temporal scales.

This pioneering graduate textbook provides readers with the concepts and practical tools required to understand the maximum entropy principle, and apply it to an understanding of ecological patterns. Rather than building and combining mechanistic models of ecosystems, the approach is grounded in information theory and the logic of inference. Paralleling the derivation of thermodynamics from the maximum entropy principle, the state variable theory of ecology developed in this book predicts realistic forms for all metrics of ecology that describe patterns in the distribution, abundance, and energetics of species over multiple spatial scales, a wide range of habitats, and diverse taxonomic groups. The first part of the book is foundational, discussing the nature of theory, the relationship of ecology to other sciences, and the concept of the logic of inference. Subsequent sections present the fundamentals of macroecology and of maximum information entropy, starting from first principles. The core of the book integrates these fundamental principles, leading to the derivation and testing of the predictions of the maximum entropy theory of ecology (METE). A final section broadens the book's perspective by showing how METE can help clarify several major issues in conservation biology, placing it in context with other theories and highlighting avenues for future research.