Stochastics

Since its inception by Perron and Frobenius, the theory of non-negative matrices has developed enormously and is now being used and extended in applied fields of study as diverse as probability theory, numerical analysis, demography, mathematical economics, and dynamic programming, while its development is still proceeding rapidly as a branch of pure mathematics in its own right.

This book aims to provide a self-contained introduction to the local geometry of the stochastic flows.

This book aims to introduce readers without a strong mathematical background to the basic ideas of fuzzy set theory and logic.

Aimed primarily at graduate students and researchers, this text is a comprehensive course in modern probability theory and its measure-theoretical foundations.

Iterative Methods for Queuing and Manufacturing Systems introduces the recent advances and developments in iterative methods for solving Markovian queuing and manufacturing problems.

Classical Extreme Value Theory-the asymptotic distributional theory for maxima of independent, identically distributed random variables-may be regarded as roughly half a century old, even though its roots reach further back into mathematical antiquity.

Many probability books are written by mathematicians and have the built-in bias that the reader is assumed to be a mathematician coming to the material for its beauty.

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty.

This book, the sixth of 15 related monographs, discusses singularity and networks of equilibriums and 1-diemsnional flows in product quadratic and cubic systems.

Develops the theory of Markov and semi-Markov processes in an elementary setting suitable for senior undergraduate and graduate students.

This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change.

With the diversification of Internet services and the increase in mobile users, efficient management of network resources has become an extremely important issue in the field of wireless communication networks (WCNs).

This book provides a rigorous introduction to the theory, computation, and applications of variational inequalities (VIs), with a focus on applications in management science and finance.

The theory of Markov chains, although a special case of Markov processes, is here developed for its own sake and presented on its own merits.

Statistical Modeling and Analysis for Complex Data Problems treats some of today's more complex problems and it reflects some of the important research directions in the field.

The 1990 Seminar on Stochastic Processes was held at the University of British Columbia from May 10 through May 12, 1990.

Stochastic Processes: General Theory starts with the fundamental existence theorem of Kolmogorov, together with several of its extensions to stochastic processes.

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.

This book extends the theory and applications of random evolutions to semi-Markov random media in discrete time, essentially focusing on semi-Markov chains as switching or driving processes.

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.

The main challenge in the study of nonautonomous phenomena is to understand the very complicated dynamical behaviour both as a scientific and mathematical problem.

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.

The essays in this volume were presented to Professor Isamu Yamada in honor of his seventy-third birthday.

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.

This book addresses a diverse set of topics of contemporary interest in statistics and data science such as biostatistics and machine learning.

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.

Entropy inequalities, correlation functions, couplingsbetween stochastic processes are powerful techniques whichhave been extensively used to give arigorous foundation tothe theory of complex, many component systems and to itsmany applications in a variety of fields as physics,biology, population dynamics, economics, .

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).