Stochastics

Scan statistics is currently one of the most active and important areas of research in applied probability and statistics, having applications to a wide variety of fields: archaeology, astronomy, bioinformatics, biosurveillance, molecular biology, genetics, computer science, electrical engineering, geography, material sciences, physics, reconnaissance, reliability and quality control, telecommunication, and epidemiology.

Probability (including stochastic processes) is now being applied to virtually every academic discipline, especially to the sciences.

This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization.

Most manufacturing systems are large, complex, and operate in an environment of uncertainty.

With the first edition out of print, we decided to arrange for republi- cation of Denumerrible Markov Ohains with additional bibliographic material.

There is currently ever pressing need to provide a critical assessment of the current knowledge and indicate new challenges which are brought by the present time in fighting the man-made and natural hazards in transient analysis of structures.

For a brief time in history, it was possible to imagine that a sufficiently advanced intellect could, given sufficient time and resources, in principle understand how to mathematically prove everything that was true.

A clear explanation of what an explosive Markov chain does after it passes through all available states in finite time.

This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993.

After that, it was down to attitude.

The nonequilibrium behavior of nanoscopic and biological systems, which are typically strongly fluctuating, is a major focus of current research.

influence on the conclusions that result.

The introductory statistics course presents serious pedagogical problems to the instructor.

The three volumes of this series of books, of which this is the second, put forward the mathematical elements that make up the foundations of a number of contemporary scientific methods: modern theory on systems, physics and engineering.

High dimensional probability, in the sense that encompasses the topics rep- resented in this volume, began about thirty years ago with research in two related areas: limit theorems for sums of independent Banach space valued random vectors and general Gaussian processes.

This is the revised and augmented edition of a now classic book which is an introduction to sub-Markovian kernels on general measurable spaces and their associated homogeneous Markov chains.

This book deals with selected problems of machine perception, using various 2D and 3D imaging sensors.

The main theme of this monograph is "e;comparative statistical inference.

DISCRETE PROBABILITY is a textbook, at a post-calculus level, for a first course in probability.

Classical statistical models for regression, time series and longitudinal data provide well-established tools for approximately normally distributed vari- ables.

With the boom of big data and machine learning and the subsequent need for parallel processing technologies, fork-join queues are more relevant now than ever before.

Diffusion Processes, Jump Processes, and Stochastic Differential Equations provides a compact exposition of the results explaining interrelations between di?

This book provides a complete exposition of equidistribution and counting problems weighted by a potential function of common perpendicular geodesics in negatively curved manifolds and simplicial trees.

Genetics mapping, physical mapping and DNA sequencing are the three key components of the human and other genome projects.

This book is intended as a text for a first course in stochastic processes at the upper undergraduate or graduate levels, assuming only that the reader has had a serious calculus course-advanced calculus would even be better-as well as a first course in probability (without measure theory).

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.

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.

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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.

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

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.

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.

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

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.