Stochastics

The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993.

Experience gained during a ten-year long involvement in modelling, program- ming and application in nonlinear optimization helped me to arrive at the conclusion that in the interest of having successful applications and efficient software production, knowing the structure of the problem to be solved is in- dispensable.

Presents a unified treatment of stochastic differential equations in abstract, mainly Hilbert, spaces.

Driven by the request for increased productivity, flexibility, and competitiveness, modern civilization increasingly has created high-performance discrete event dynamic systems (DEDSs).

The consecutive-k system was first studied around 1980, and it soon became a very popular subject.

The book explores the field of model predictive control (MPC).

With the rapid progress and development of mathematical statistical methods, it is becoming more and more important for the student, the in- structor, and the researcher in this field to have at their disposal a quick, comprehensive, and compact reference source on a very wide range of the field of modern mathematical statistics.

This book explains the basic theory of Hilbert C*-module in detail, covering a wide range of applications from generalized index to module framework.

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Great advances have been made in recent years in the field of computational probability.

This book provides probabilists with sufficient background to begin applying PDEs to probability theory and probability theory to PDEs.

This textbook provides a comprehensive exploration of anomalous stochastic processes and extreme events, commonly referred to as "e;black swans,"e; with a particular focus on (multi-)fractal approaches and continuous-time random walks.

Provides a comprehensive analysis of stochastic dominance through coverage of concepts, methods of estimation, inferential tools, and applications.

This book systematically presents the topological structure of solution sets and attractability for nonlinear evolution inclusions, together with its relevant applications in control problems and partial differential equations.

This book offers a novel multi-portfolio approach and stochastic programming formulations for modeling and solving contemporary supply chain risk management problems.

An introduction to stochastic processes through the use of R Introduction to Stochastic Processes with R is an accessible and well-balanced presentation of the theory of stochastic processes, with an emphasis on real-world applications of probability theory in the natural and social sciences.

With the rapidly increasing penetration of laptop computers and mobile phones, which are primarily used by mobile users to access Internet s- vices like e-mail and World Wide Web (WWW) access, support of Internet services in a mobile environment is an emerging requirement.

This book is designed as a text for graduate courses in stochastic processes.

This book is based mainly on the lecture notes that I have been using since 1993 for a course on applied probability for engineers that I teach at the Ecole Polytechnique de Montreal.

Conventional statistical methods have a very serious flaw: They routinely miss differences among groups or associations among variables that are detected by more modern techniques - even under very small departures from normality.

This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation?

Random matrices are widely and successfully used in physics for almost 60-70 years, beginning with the works of Dyson and Wigner.

"e;Following in the footsteps of Stephen Hawking's 'A brief history of time' and Simon Singh's 'Fermat's Last Theorem' this exceptionally accessible book will you leave marveling at the wonders of the world and, if you didn't listen to your science teachers, wishing you had.

Featuring previously unpublished results, Semi-Markov Models: Control of Restorable Systems with Latent Failures describes valuable methodology which can be used by readers to build mathematical models of a wide class of systems for various applications.

The book presents an introduction to Stochastic Processes including Markov Chains, Birth and Death processes, Brownian motion and Autoregressive models.

The problem of designing a cost-efficient network thatsurvives the failure of one or more nodes or edges of thenetwork is critical to modern telecommunicationsengineering.

Malliavin Calculus in Finance: Theory and Practice aims to introduce the study of stochastic volatility (SV) models via Malliavin Calculus.

White Noise Calculus is a distribution theory on Gaussian space, proposed by T.

Solutions Manual to accompany Fundamentals of Matrix Analysis with Applications an accessible and clear introduction to linear algebra with a focus on matrices and engineering applications.

This book is intended to be a useful contribution for the modern teaching of applied mathematics, educating Industrial Mathematicians that will meet the growing demand for such experts.

Two of the most fundamental concepts in the theory of stochastic processes are the Markov property and the martingale property.

The Theory and Practice of Revenue Management is a book that comprehensively covers theory and practice of the entire field, including both quantity and price-based RM, as well as significant coverage of supporting topics such as forecasting and economics.

Scientists and engineers are increasingly making use of simulation methods to solve problems which are insoluble by analytical techniques.

This book was created with the goal of helping students transition from the theoretical and methodological concepts of statistical inference to their implementation on a computer.

"e;Following in the footsteps of Stephen Hawking's 'A brief history of time' and Simon Singh's 'Fermat's Last Theorem' this exceptionally accessible book will you leave marveling at the wonders of the world and, if you didn't listen to your science teachers, wishing you had.

The series is designed to bring together those mathematicians who are seriously interested in getting new challenging stimuli from economic theories with those economists who are seeking effective mathematical tools for their research.

This book focuses on recent advances, approaches, theories and applications related to mixture models.

This fourth edition contains several additions.

Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends.

Theory of Markov Processes provides information pertinent to the logical foundations of the theory of Markov random processes.

This book provides a theoretical background of branching processes and discusses their biological applications.

Point processes and random measures find wide applicability in telecommunications, earthquakes, image analysis, spatial point patterns, and stereology, to name but a few areas.

This book does not focus solely on asymptotic extreme value distributions.

This textbook reconstructs the statistics curriculum from the perspective of posterior probability.

This book deals with one of the fundamental problems of nonequilibrium statistical mechanics: the explanation of large-scale dynamics (evolution differential equations) from models of a very large number of interacting particles.

This monograph provides a comprehensive overview of locally perturbed random walks, tools used for their analysis, and current research on their applications.