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Linear Optimization Problems with Inexact Data
Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems—for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average” values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.
Linear Models for Optimal Test Design
Begins with a reflection on the history of test design--the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples. The presentation includes models for the assembly of tests to an absolute or relative target for their information functions, classical test assembly, test equating problems, item matching, test splitting, simultaneous assembly of multiple tests, tests with item sets, multidimensional tests, and adaptive test assembly. Two separate chapters are devoted to the questions of how to design item banks for optimal support of programs with fixed and adaptive tests. Linear Models for Optimal Test Design, which does not require any specific mathematical background, has been written to be a helpful resource on the desk of any test specialist.
Linear Models and Generalizations : Least Squares and Alternatives
Gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions
Linear Estimation and Detection in Krylov Subspaces
Focuses on the foundations of linear estimation theory which is essential for effective signal processing. In its first part, it gives a comprehensive overview of several key methods like reduced-rank signal processing and Krylov subspace methods of numerical mathematics. Based on the derivation of the multistage Wiener filter in its most general form, the relationship between statistical signal processing and numerical mathematics is presented. In the second part, the theory is applied to iterative multiuser detection receivers (Turbo equalization) which are typically desired in wireless communication systems.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
Light Scattering Reviews 2
Part of the ""Light Scattering Reviews"" series that is devoted to modern knowledge and milestones in both experimental and theoretical techniques related to light scattering and radiative transport problems. This work gives a picture of developments in the area of remote sensing and radiative transfer.
Lifetime Spectroscopy : A Method of Defect Characterization in Silicon for Photovoltaic Applications
Lifetime spectroscopy is one of the most sensitive diagnostic tools for the identification and analysis of impurities in semiconductors. Since it is based on the recombination process, it provides insight into precisely those defects that are relevant to semiconductor devices such as solar cells. This book introduces a transparent modeling procedure that allows a detailed theoretical evaluation of the spectroscopic potential of the different lifetime spectroscopic techniques. The various theoretical predictions are verified experimentally with the context of a comprehensive study on different metal impurities. The quality and consistency of the spectroscopic results, as explained here, confirms the excellent performance of lifetime spectroscopy.
Lie Algebras and Applications
This book, designed for advanced graduate students and post-graduate researchers, provides an introduction to Lie algebras and some of their applications to the spectroscopy of molecules, atoms, nuclei and hadrons. In the first part, a concise exposition is given of the basic concepts of Lie algebras, their representations and their invariants. The second part contains a description of how Lie algebras are used in practice in the treatment of bosonic and fermionic systems. Physical applications considered include rotations and vibrations of molecules (vibron model), collective modes in nuclei (interacting boson model), the atomic shell model, the nuclear shell model, and the quark model of hadrons. One of the key concepts in the application of Lie algebraic methods in physics, that of spectrum generating algebras and their associated dynamic symmetries, is also discussed. The book contains many examples that help to elucidate the abstract algebraic definitions. It provides a summary of many formulas of practical interest, such as the eigenvalues of Casimir operators and the dimensions of the representations of all classical Lie algebras.
Lewis Fry Richardson : His Intellectual Legacy and Influence in the Social Sciences
A pioneer in meteorology and peace research and remains a towering presence in both fields. This edited volume reviews his work and assesses its influence in the social sciences, notably his work on arms races and their consequences, mathematical models, the size distribution of wars, and geographical features of conflict
Level Crossing Methods in Stochastic Models
Since its inception in 1974, the level crossing approach for analyzing a large class of stochastic models has become increasingly popular among researchers. This volume traces the evolution of level crossing theory for obtaining probability distributions of state variables and demonstrates solution methods in a variety of stochastic models including: queues, inventories, dams, renewal models, counter models, pharmacokinetics, and the natural sciences. Results for both steady-state and transient distributions are given, and numerous examples help the reader apply the method to solve problems faster, more easily, and more intuitively.
Lectures on Probability Theory and Statistics : Ecole d'Eté de Probabilités de Saint-Flour XXXIII - 2003
Contains two of the three lectures that were given at the 33rd Probability Summer School in Saint-Flour (July 6-23, 2003). Amir Dembo’s course is devoted to recent studies of the fractal nature of random sets, focusing on some fine properties of the sample path of random walk and Brownian motion. In particular, the cover time for Markov chains, the dimension of discrete limsup random fractals, the multi-scale truncated second moment and the Ciesielski-Taylor identities are explored. Tadahisa Funaki’s course reviews recent developments of the mathematical theory on stochastic interface models, mostly on the so-called nabla varphi interface model. The results are formulated as classical limit theorems in probability theory, and the text serves with good applications of basic probability techniques.
Lectures in Supercomputational Neurosciences : Dynamics in Complex Brain Networks
The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research.
Lectures in Supercomputational Neurosciences : Dynamics in Complex Brain Networks
The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research.
Learning in Modern International Society : On the Cognitive Problem Solving Abilities of Political Actors
Claudia Hofmann engages in a theoretical modelling of international learning processes and the substantiation of this model through three cases from international politics. She answers two questions: How may international actors learn as a collective? And how may the lessons learned influence actor behaviour and problem solving processes? As a foundation for answering these questions she examines the nature of actor behaviour within a social international system and integrates the diffusion of norms and values among macro-level actors.
Learning Classifier Systems in Data Mining
Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains.
Learning About Particles - 50 Privileged Years
Embedded in an autobiographic framework, this book retraces vividly and in some depth the golden years of particle physics as witnessed by one of the scientists who made seminal contributions to the understanding of what is now known as the Standard Model of particle physics. Well beyond a survey of interest to historians of sciences and researchers in the field, this book is a must for all students and young researchers who have learned about the theoretical and experimental facts that make up the standard model through modern textbooks only. It will provide the interested reader with a first hand account and deeper understanding of the multilayered and sinuous development that finally led to the present architecture of this theory.
Lead-Free Electronic Solders : A Special Issue of the Journal of Materials Science : Materials in Electronics
In the last few decades the effect of lead contamination on human health has received significant attention. Based on such concerns, elimination of lead from ceramic glaze, paint, plumbing etc. has been legislated and implemented. However, until recently, solders used in electronics, based on suitability and knowledge-base developed over a long period of time, remained lead-based. Successive rapid advances in microelectronic devices in recent decades make them obsolete within a very short period after their introduction resulting in significant quantities of electronic wastes in landfills. Leaching of toxic lead from such electronic wastes can result in contamination of the human food chain causing serious health hazards. As a consequence, several European and Pacific Rim countries have passed legislations warranting elimination of lead from electronic solders by fast approaching deadlines. Global economic pressures brought on by such legislations have resulted in a flurry of research activities to find suitable lead-free substitutes for the traditional leaded electronic solders.
Leadership and Business Ethics ; 1st ed.
Seeks to contribute to a more adequate coalescence of ethics and business with innovative models for such coalescence, for the mutual benefit of business ethicists, professors teaching in the undergraduate and MBA classroom, corporate executives, and businesspeople. While each of the contributions in this collection is distinct, each invites us to examine our own mind sets about corporate responsibility and the future of free enterprise as Western multinational corporations expand into a global economy. This collection of essays helps to refocus our conceptual work about commerce and business practices in this new century of global enterprise.
Le raisonnement bayésien : Modélisation et inférence = Bayesian reasoning : Modeling and inference
Describes in detail the practice of the Bayesian statistical approach using many examples chosen for their educational interest. The first part gives the general principles of statistical modeling making it possible to supervise but also to come to the aid of the imagination of the apprentice modeler. By examining examples of increasing difficulty, the reader forges the keys to building their own model. The second part presents the most useful calculation algorithms for estimating the unknowns of the model. Each inference method is presented and illustrated by numerous application cases.
Le choix bayésien: Principes et pratique
Covers the so-called Bayesian approach to statistical inference and in particular its decision-making aspects. The bases of this axiomatics (choice of the a priori, optimal decisions, tests and regions of confidence) are discussed in detail, as well as more recent openings of Bayesian analysis such as the choice of models, the use of numerical methods. Stochastic approximation (MCMC), the theory of noninformative laws (Berger-Bernardo axioms) and the relation to the classical theory of admissibility. Each chapter is completed by an extensive series of exercises of increasing difficulty and by bibliographical notes on the themes addressed. This book can be used in a Master's program in Applied Mathematics, Biometrics, Econometrics or any other program that uses quantitative information processing techniques. It only requires a basic course in probability theory and mathematical statistics as a preliminary.
