الصفحة 11
الصفحة 11
IP Traffic Theory and Performance
This book presents different approaches in IP traffic theory and classifies them, especially towards applications in the Internet. It comprises the state of the art in this area, which is currently presented only by numerous research papers and overview articles. The book provides an ideal starting point for detailed studies of traffic analysis in IP networks. It gives the reader the possibility to judge on different models and to select the appropriate for his individual needs in applications.
Invexity and Optimization
Invexity and Optimization presents results on invex function and their properties in smooth and nonsmooth cases, pseudolinearity and eta-pseudolinearity. Results on optimality and duality for a nonlinear scalar programming problem are presented, second and higher order duality results are given for a nonlinear scalar programming problem, and saddle point results are also presented. Invexity in multiobjective programming problems and Kuhn-Tucker optimality conditions are given for a multiobjecive programming problem, Wolfe and Mond-Weir type dual models are given for a multiobjective programming problem and usual duality results are presented in presence of invex functions. Continuous-time multiobjective problems are also discussed. Quadratic and fractional programming problems are given for invex functions. Symmetric duality results are also given for scalar and vector cases.
Investment valuation and asset pricing : Models and methods
Offers an overview of original works on foundational asset pricing studies that follows their historical publication chronologically throughout the text. Each chapter stays close to the original works of these major authors, including quotations, examples, graphical exhibits, and empirical results. Additionally, it includes statistical concepts and methods as applied to finance. These statistical materials are crucial to learning asset pricing, which often applies statistical tests to evaluate different asset pricing models.
Investment Appraisal : Methods and Models
Investment decisions are of vital importance to all companies. Getting these decisions right is crucial but, due to a complex and dynamic business environment, this remains a challenging management task. Effective appraisal methods are valuable tools in supporting investment decision-making. As organisations continue to seek a competitive edge, it is increasingly important that management accountants and strategic decision-makers have a sound knowledge of these tools. This book presents a range of investment appraisal methods and models, some of which are not widely known, or at least not well covered in other books. Each approach is thoroughly described, evaluated and illustrated using examples, with its assumptions and limitations analysed in terms of their implications for investment decision-making practice.
Inverse Problems in Vibration
In this new edition the scope of the book has been widened to include topics such as isospectral systems- families of systems which all exhibit some specified behaviour; applications of the concept of Toda flow; new, non-classical approaches to inverse Sturm-Liouville problems; qualitative properties of the modes of some finite element models; damage identification.
Inventory and Supply Chain Management with Forecast Updates
Inventory and Supply Chain Management with Forecast Updates is concerned with the problems of inventory and supply chain decision making with information updating over time. The models considered include inventory decisions with multiple sources and delivery modes, supply-contract design and evaluation, contracts with exercise price, volume-flexible contracts allowing for spot-market purchase decisions, and competitive supply chains. Real problems are formulated into tractable mathematical models, which allow for an analysis of various approaches, and provide insights for better supply chain management. The book provides a unified treatment of these models, presents a critique of the existing results, and points out potential research directions. Attention is focused on solutions – that is, inventory decisions prior and subsequent to information updates and the impact of the quality of information on these decisions.
Invariant Manifolds for Physical and Chemical Kinetics
By bringing together various ideas and methods for extracting the slow manifolds the authors show that it is possible to establish a more macroscopic description in nonequilibrium systems. The book treats slowness as stability. A unifying geometrical viewpoint of the thermodynamics of slow and fast motion enables the development of reduction techniques, both analytical and numerical. Examples considered in the book range from the Boltzmann kinetic equation and hydrodynamics to the Fokker-Planck equations of polymer dynamics and models of chemical kinetics describing oxidation reactions. Special chapters are devoted to model reduction in classical statistical dynamics, natural selection, and exact solutions for slow hydrodynamic manifolds. The book will be a major reference source for both theoretical and applied model reduction. Intended primarily as a postgraduate-level text in nonequilibrium kinetics and model reduction, it will also be valuable to PhD students and researchers in applied mathematics, physics and various fields of engineering.
Introductory Lectures on Fluctuations of Lévy Processes with Applications
Lévy processes are the natural continuous-time analogue of random walks and form a rich class of stochastic processes around which a robust mathematical theory exists. Their mathematical significance is justified by their application in many areas of classical and modern stochastic models including storage models, renewal processes, insurance risk models, optimal stopping problems, mathematical finance and continuous-state branching processes.The book aims to be mathematically rigorous while still providing an intuitive feel for underlying principles. The results and applications often focus on the case of Lévy processes with jumps in only one direction.
Introduction to Stochastic Calculus for Finance : A New Didactic Approach
The justifcation is mainly pedagogical. These lecture notes start with an elementary approach to stochastic calculus due to Föllmer, who showed that one can develop Ito's calculus "pathwise" as an exercise in real analysis. The text opens to students interested in finance a quick (but by no means "dirty") road to the tools required for advanced finance in continuous time, including option pricing by martingale methods, term structure models in a HJM-framework and the Libor market model.
Introduction to Modern Time Series Analysis
This excellent textbook presents an introduction to the time series analysis. It provides a good source of information for graduate and master students in economics and statistics. It is a well-written and easy to read book, illustrated by 56 good examples. Also, many important references are listed at the end of each chapter.This book presents to beginners a readable and easily accessible introduction to modern developments in time series econometrics and financial time series with an emphasis on basic concepts and practical applications. The book is a textbook consisting of seven chapters the greatest merit of this textbook is that it enables readers to grasp the basic framework of time-series econometrics without relying on extensive reading
Introduction to Mathematical Methods in Bioinformatics
This book looks at the mathematical foundations of the models currently in use. This book is unique in the sense that it looks at the mathematical foundations of the models, which are crucial for correct interpretation of the outputs of the models.
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
Introduction to Empirical Processes and Semiparametric Inference
This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level of the book is suitable for a second year graduate course in statistics or biostatistics, provided the students have had a year of graduate level mathematical statistics and a semester of probability.
Introduction to Drug Disposition and Pharmacokinetics
The application of knowledge of drug disposition, and skills in pharmacokinetics, are crucial to the development of new drugs and to a better understanding of how to achieve maximum benefit from existing ones. The book takes the reader from basic concepts to a point where those who wish to will be able to perform pharmacokinetic calculations and be ready to read more advanced texts and research papers.
Introduction to data systems : Building from Python
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.
Introduction to Computational Optimization Models for Production Planning in a Supply Chain
In this book we strive to provide models that capture many of the - tails faced by ?rms operating in a modern supply chain, but we stop short of proposing models for economic analysis of the entire multi-player chain. In other words, we produce models that are useful for planning within a supply chain rather than models for planning the supply chain. The usefulness of the models is enhanced greatly by the fact that they have been implemented - ing computer modeling languages. Implementations are shown in Chapter 7, which allows solutions to be found using a computer.
Introduction to Computational Biology : An Evolutionary Approach
Molecular biology has changed dramatically over the past two decades. Until the early 1990s genes were studied one at a time by small teams of researchers; today entire genomes are sequenced by internationally collaborating laboratories. In the bygone gene-centered era the accumulation of data was the rate-limiting step in research. Now that step is often data interpretation. This is increasingly dependent on computational methods and as a consequence, computational biology has emerged in the past decade as a new subdiscipline of biology. This introduction to computational biology is centered on the analysis of molecular sequence data. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors.
Introduction to Bayesian Statistics
This is the second and translated edition of the German book “Einf ̈uhrung in die Bayes-Statistik, Springer-Verlag, Berlin Heidelberg New York, 2000”. It has been completely revised and numerous new developments are pointed out together with the relevant literature. The Chapter 5.2.4 is extended by the stochastic trace estimation for variance components. The new Chapter 5.2.6 presents the estimation of the regularization parameter of type Tykhonov regularization for inverse problems as the ratio of two variance components.The reconstruction and the smoothing of digital three-dimensional images is demonstrated in the new Chapter 5.3. The Chapter 6.2.1 on importance sampling for the Monte Carlo integration is rewritten to solve a more general integral. This chapter contains also the derivation of the SIR (sampling-importance-resampling) algorithm as an alternative to the rejection method for generating random samples. Markov Chain Monte Carlo methods are now frequently applied in Bayesian statistics.
Introduction to applied mathematics for environmental science
Introduction to Mathematics for Environmental Science evolved from the author’s 30 years’ experience teaching mathematics to graduate and advanced undergraduate students in the environmental sciences. Its basic purpose is to teach various types of mathematical structures and how they can be applied in a broad range of environmental science subfields. Derivatives and integrals, ordinary and partial differential equations, and linear and non-linear algebraic equations are the basic kinds of structures (types of mathematical models) discussed.
