This book is about phylogenetic diversity as an approach to reduce biodiversity losses in this period of mass extinction. Chapters in the first section deal with questions such as the way we value phylogenetic diversity among other criteria for biodiversity conservation; the choice of measures; the loss of phylogenetic diversity with extinction; the importance of organisms that are deeply branched in the tree of life, and the role of relict species. The second section is composed by contributions exploring methodological aspects, such as how to deal with abundance, sampling effort, or conflicting trees in analysis of phylogenetic diversity. The last section is devoted to applications, showing how phylogenetic diversity can be integrated in systematic conservation planning, in EDGE and HEDGE evaluations. This wide coverage makes the book a reference for academics, policy makers and stakeholders dealing with biodiversity conservation.
This Is The Fourth Volume Of Selected Papers Of C. R. Rao Consisting Of 39 Papers Published During 1975-1985. These Papers Represent The Development Of Some Of The Basic Concepts Proposed By The Author In The Fields Of Unified Theory Of Least Squares Estimation, Weighted Distributions, Bayesian Statistical Inference, Generalised Inverses Of Matrices And Their Applications In Which Contemporary Research Is Carried Out Extensively. Work On Solutions Of Functional Equations And Their Application In Characterizations Of Distributions Is Also Of Current Interest. Introduction Of Measures Of Diversity, Quadratic Entropy And Allied Concepts Find Applications In Various Fields Such As Anthropology And Social Sciences. As In The Earlier Volumes, The Papers That Have Originally Appeared In Different Publications Have Been Retypeset To Maintain Uniformity In Presentation.The Final Volume With More Papers, An Updated Bibliography Of Works And A Comprehensive Overview Of The Total Opus Of Professor C. R. Rao Is Going To Come Out Soon.
The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this p
This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.
This book contains the papers presented at the 2nd IPMU Conference, held in Urbino (Italy), on July 4-7, 1988. The theme of the conference, Management of Uncertainty and Approximate Reasoning, is at the heart of many knowledge-based systems and a number of approaches have been developed for representing these types of information. The proceedings of the conference provide, on one hand, the opportunity for researchers to have a comprehensive view of recent results and, on the other, bring to the attention of a broader community the potential impact of developments in this area for future generation knowledge-based systems. The main topics are the following: frameworks for knowledge-based systems: representation scheme, neural networks, parallel reasoning schemes; reasoning techniques under uncertainty: non-monotonic and default reasoning, evidence theory, fuzzy sets, possibility theory, Bayesian inference, approximate reasoning; information theoretical approaches; knowledge acquisition and automated learning.
Information is precious. It reduces our uncertainty in making decisions. Knowledge about the outcome of an uncertain event gives the possessor an advantage. It changes the course of lives, nations, and history itself. Information is the food of Maxwell's demon. His power comes from know ing which particles are hot and which particles are cold. His existence was paradoxical to classical physics and only the realization that information too was a source of power led to his taming. Information has recently become a commodity, traded and sold like or ange juice or hog bellies. Colleges give degrees in information science and information management. Technology of the computer age has provided access to information in overwhelming quantity. Information has become something worth studying in its own right. The purpose of this volume is to introduce key developments and results in the area of generalized information theory, a theory that deals with uncertainty-based information within mathematical frameworks that are broader than classical set theory and probability theory. The volume is organized as follows.
The present book may be regarded as a successor of author's Maximum Entropy Models in Science and Engineering (Wiley), Generalized Maximum Entropy Principle (Sandford), Entropy Optimization Principles and Their Applications (Academic) and Insight into Entropy Optimizations Principles (MSTS). It contains sixty research investigations of the author on measures of entropy, directed divergence, weighted directed divergence, information, principles of maximum entropy, minimum entropy, minimum cross-entropy, minimum entropy, minimum information, minimum weighted information and maximum weighted entropy, most likely and most feasible distributions, duals of optimization problems, entropy optimization under inequality constraints, characterising moments, parameter estimation, maximum entropy approximation for a probability distribution, proving inequalities, laws of information, entropic mean, mean-entropy frontier, logistic-type growth models, birth-death processes, distributions of statistical mechanics, estimation of missing values, theorems of information theory and many others.
Stochastic portfolio theory is a mathematical methodology for constructing stock portfolios and for analyzing the effects induced on the behavior of these portfolios by changes in the distribution of capital in the market. Stochastic portfolio theory has both theoretical and practical applications: as a theoretical tool it can be used to construct examples of theoretical portfolios with specified characteristics and to determine the distributional component of portfolio return. This book is an introduction to stochastic portfolio theory for investment professionals and for students of mathematical finance. Each chapter includes a number of problems of varying levels of difficulty and a brief summary of the principal results of the chapter, without proofs.
Statistical Decision Theory and Related Topics III, Volume 2 is a collection of papers presented at the Third Purdue Symposium on Statistical Decision Theory and Related Topics, held at Purdue University in June 1981. The symposium brought together many prominent leaders and a number of younger researchers in statistical decision theory and related areas. This volume contains the research papers presented at the symposium and includes works on general decision theory, multiple decision theory, optimum experimental design, sequential and adaptive inference, Bayesian analysis, robustness, and large sample theory. These research areas have seen rapid developments since the preceding Purdue Symposium in 1976, developments reflected by the variety and depth of the works in this volume. Statisticians and mathematicians will find the book very insightful.