Statistical Complexity

Statistical Analysis in the Social Sciences imbeds basic descriptive and inferential statistical concepts within a unified framework: the linear regression model. The regression model is used not only

Exploring the application and formulation of the EM algorithm, The EM Algorithm and Related Statistical Models offers a valuable method for constructing statistical models when only incomplete informa

We live in a moment of unprecedented complexity, an era in which change and information can move faster than our ability to comprehend them. With The Moment of Complexity, Mark C. Taylor offers a map

This monograph, suitable for use as an advanced text, presents the statistical mechanics of solids from the perspective of the material properties of the solid state. The statistical mechanics are dev

The science of complexity is concerned with the study of complex, adaptive systems. Its insights have in recent decades been applied with gusto by social scientists and other thinkers.As research in a

An up-to-date and critical analysis of how archaeologists study past societies, Archaeologies of Complexity addresses the nature of contemporary archaeology and the study of social change, and debates

This statistical handbook is designed to offer students, researchers, practitioners, and the general adult population a comprehensive statistical overview of the status of infectious disease worldwid

Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals introduces many of the practical adaptive statistical methods developed over the last 10 years and provides a compr

Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerf

Statistical mechanics is the theoretical apparatus used to study the properties of macroscopic systems - systems made up of many atoms or molecules - and relates those properties to the system's microscopic constitution. This book is an introduction to statistical mechanics, intended to be used either by advanced undergraduates or by beginning graduate students. The first chapter deals with statistical thermodynamics and aims to quickly derive the most commonly used formulas in the subject. The remainder of the book then illustrates the application of these formulas in traditional areas such as the ideal gas and less traditional areas such as the quantum ideal gas. Highly illustrated with numerous exercises and worked solutions, it provides a concise treatise of statistical mechanics ideal for use on an 8-12 lecture course.

Statistical mechanics is the theoretical apparatus used to study the properties of macroscopic systems - systems made up of many atoms or molecules - and relates those properties to the system's microscopic constitution. This book is an introduction to statistical mechanics, intended to be used either by advanced undergraduates or by beginning graduate students. The first chapter deals with statistical thermodynamics and aims to quickly derive the most commonly used formulas in the subject. The remainder of the book then illustrates the application of these formulas in traditional areas such as the ideal gas and less traditional areas such as the quantum ideal gas. Highly illustrated with numerous exercises and worked solutions, it provides a concise treatise of statistical mechanics ideal for use on an 8-12 lecture course.

An incomparably useful examination of statistical methods for comparisonThe nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of s

This book is intended for the statistician or student interested in becoming a statistical consultant and shows how to communicate statistical results effectively. It clearly illustrates what the con

The discrepancy method is the glue that binds randomness and complexity. It is the bridge between randomized computation and discrepancy theory, the area of mathematics concerned with irregularities in distributions. The discrepancy method has played a major role in complexity theory; in particular, it has caused a mini-revolution of sorts in computational geometry. This book tells the story of the discrepancy method in a few short independent vignettes. It is a varied tale which includes such topics as communication complexity, pseudo-randomness, rapidly mixing Markov chains, points on the sphere and modular forms, derandomization, convex hulls, Voronoi diagrams, linear programming and extensions, geometric sampling, VC-dimension theory, minimum spanning trees, linear circuit complexity, and multidimensional searching. The mathematical treatment is thorough and self-contained. In particular, background material in discrepancy theory is supplied as needed. Thus the book should appeal t

Here is an accessible, algorithmically oriented guide to some of the most interesting techniques of complexity theory. The book shows that simple algorithms are at the heart of complexity theory. The

This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniqu

Fast track route to mastering highly complex environments Covers the fundamentals of complexity, from encouraging iconoclastic behavior and setting up formal complexity training to using scenarios an

A groundbreaking look at complexity theory and its implications in the world of finance Complexity theory tells us that processes with a large number of seemingly independent agents-such as free marke

Presents statistical principles in a simple fashion, and develops an awareness of the abuses and misuses of statistics, combining coverage of research and statistical practices. Early chapters centere

Thermodynamics and Statistical Mechanics provides undergraduate chemistry students with a grounding in both classical and statistical thermodynamics. Thermodynamic quantities and relationships are int

The discrepancy method is the glue that binds randomness and complexity. It is the bridge between randomized computation and discrepancy theory, the area of mathematics concerned with irregularities in distributions. The discrepancy method has played a major role in complexity theory; in particular, it has caused a mini-revolution of sorts in computational geometry. This book tells the story of the discrepancy method in a few short independent vignettes. It is a varied tale which includes such topics as communication complexity, pseudo-randomness, rapidly mixing Markov chains, points on the sphere and modular forms, derandomization, convex hulls, Voronoi diagrams, linear programming and extensions, geometric sampling, VC-dimension theory, minimum spanning trees, linear circuit complexity, and multidimensional searching. The mathematical treatment is thorough and self-contained. In particular, background material in discrepancy theory is supplied as needed. Thus the book should appeal t

This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number o

A thorough understanding of statistical mechanics depends strongly on the insights and manipulative skills that are acquired through the solving of problems. Problems on Statistical Mechanics provides

Many of the exciting new developments in microscale engineering are based on the application of traditional principles of statistical thermodynamics. This book offers a modern view of thermodynamics, interweaving classical and statistical thermodynamic principles and applying them to current engineering systems. It begins with coverage of microscale energy storage mechanisms from a quantum mechanics perspective and develops the fundamentals of classical and statistical thermodynamics. Next, applications of equilibrium statistical thermodynamics to solid, liquid, and gas phase systems are discussed. The remainder of the book discusses nonequilibrium thermodynamics of transport phenomena and introduces nonequilibrium effects and noncontinuum behaviour at the microscale. Although the text emphasizes mathematical development, it includes many examples and exercises illustrating how the theoretical concepts are applied to systems of scientific and engineering interest. It offers a fresh vie

This is a comprehensive discussion of complexity as it arises in physical, chemical, and biological systems, as well as in mathematical models of nature. Common features of these apparently unrelated fields are emphasised and incorporated into a uniform mathematical description, with the support of a large number of detailed examples and illustrations. The quantitative study of complexity is a rapidly developing subject with special impact in the fields of physics, mathematics, information science, and biology. Because of the variety of the approaches, no comprehensive discussion has previously been attempted. This book will be of interest to graduate students and researchers in physics (nonlinear dynamics, fluid dynamics, solid-state, cellular automata, stochastic processes, statistical mechanics and thermodynamics), mathematics (dynamical systems, ergodic and probability theory), information and computer science (coding, information theory and algorithmic complexity), electrical engi

Clearly written and free of statistical jargon, this invaluable guide concentrates on the practicalities of statistical analysis for anyone involved with agricultural research. Each section starts wi

This 1998 book describes the sampling and statistical methods used most often by behavioral ecologists and field biologists. Written by a biologist and two statisticians, it provides a rigorous discussion together with worked examples of statistical concepts and methods that are generally not covered in introductory courses, and which are consequently poorly understood and applied by field biologists. The first section reviews important issues such as defining the statistical population and the sampling plan when using non-random methods for sample selection, bias, interpretation of statistical tests, confidence intervals and multiple comparisons. After a detailed discussion of sampling methods and multiple regression, subsequent chapters discuss specialized problems such as pseudoreplication, and their solutions. It will quickly become the statistical handbook for all field biologists.

This 1998 book describes the sampling and statistical methods used most often by behavioral ecologists and field biologists. Written by a biologist and two statisticians, it provides a rigorous discussion together with worked examples of statistical concepts and methods that are generally not covered in introductory courses, and which are consequently poorly understood and applied by field biologists. The first section reviews important issues such as defining the statistical population and the sampling plan when using non-random methods for sample selection, bias, interpretation of statistical tests, confidence intervals and multiple comparisons. After a detailed discussion of sampling methods and multiple regression, subsequent chapters discuss specialized problems such as pseudoreplication, and their solutions. It will quickly become the statistical handbook for all field biologists.

An advanced textbook giving a broad, modern view of the computational complexity theory of boolean circuits, with extensive references, for theoretical computer scientists and mathematicians.

An approach to complexity theory which offers a means of analysing algorithms in terms of their tractability. The authors consider the problem in terms of parameterized languages and taking "k-slices"

The text is written for technique oriented courses on quality like Introduction to Statistical Quality Control/Process Improvement that stress the use of statistical tools.In addition to the statistic

In Complexity and Postmodernism, Paul Cilliers explores the idea of complexity in the light of contemporary perspectives from philosophy and science. Cilliers offers us a unique approach to understand

Statistical Mechanics reflects the latest techniques and developments in statistical mechanics. Covering a variety of concepts and topics - molecular dynamic methods, renormalization theory, chaos, po

Interpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data i

Proceedings of the December 1996 symposium, with sections on statistical mechanics of protein and of DNA, biologically inspired physics, statistical mechanics in biology and physics, micromanipulation

A computer science text bridging the gap between the perceived impenetrability of computability and complexity theory and practical programming problems. Jones (computer science, U. of Copenhagen) of

A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the lan

The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related met

In this volume, which was originally published in 1996, noisy information is studied in the context of computational complexity; in other words the text deals with the computational complexity of mathematical problems for which information is partial, noisy and priced. The author develops a general theory of computational complexity of continuous problems with noisy information and gives a number of applications; deterministic as well as stochastic noise is considered. He presents optimal algorithms, optimal information, and complexity bounds in different settings: worst case, average case, mixed worst-average and average-worst, and asymptotic. The book integrates the work of researchers in such areas as computational complexity, approximation theory and statistics, and includes many fresh results as well. About two hundred exercises are supplied with a view to increasing the reader's understanding of the subject. The text will be of interest to professional computer scientists, statis

Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory of factor analysis, this new work represents the first unifi