Stochastic Programing

Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuse

Uncertainty has been a concern to engineers, managers, and scientists for many years in engineering and sciences. Uncertainty has for a long time been considered synonymous with random, stochastic, s

Moving on from earlier stochastic and robust control paradigms, this book introduces the fundamentals of probabilistic methods in the analysis and design of uncertain systems. The use of randomized a

The theory of Lévy processes in Lie groups is not merely an extension of the theory of Lévy processes in Euclidean spaces. Because of the unique structures possessed by non-commutative Lie groups, these processes exhibit certain interesting limiting properties which are not present for their counterparts in Euclidean spaces. These properties reveal a deep connection between the behaviour of the stochastic processes and the underlying algebraic and geometric structures of the Lie groups themselves. The purpose of this work is to provide an introduction to Lévy processes in general Lie groups, the limiting properties of Lévy processes in semi-simple Lie groups of non-compact type and the dynamical behavior of such processes as stochastic flows on certain homogeneous spaces. The reader is assumed to be familiar with Lie groups and stochastic analysis, but no prior knowledge of semi-simple Lie groups is required.

Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics methods for variography and spatial predictions, approaches to conditional stochastic simulation and local proba

The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mat

Kiyosi Ito's greatest contribution to probability theory may be his introduction of stochastic differential equations to explain the Kolmogorov-Feller theory of Markov processes. Starting with the geo

This book provides an up-to-date introduction to the theory of sound propagation in the ocean. The text treats both ray and wave propagation and pays considerable attention to stochastic problems such

This systematic book covers in simple language the physical foundations of evolution equations, stochastic processes and generalized Master equations applied on complex economic systems, helping to un

The first part is devoted to colloidal particles and stochastic dynamics, mainly concerned with recent authoritative results in the study of interactions between colloidal particles and transport prop

Long ago （or so it seems today）, Chung wrote on page 196 of his book [1]:One wonders if the present theory of stochastic processes is not still too difficult for applications. Advances in the theory s

The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. When measured through linear correlation, many of these systems exhibit a slow correlation decay

This second volume includes research contributions on the use of dynamical systems and stochastic models in disease dynamics. Graduate students in applied mathematics, scientists in the natural, soci

This book presents a cogent description of the main methodologies used in derivatives pricing. Starting with a summary of the elements of Stochastic Calculus, Quantitative Methods in Derivatives Prici

The purpose of the book is to present the basic results in the theory of two-person zero-sum repeated games including stochastic games and repeated games with incomplete information. It underlines the

An accessible introduction to probability, stochastic processes, and statistics for computer science and engineering applications This updated and revised edition of the popular classic relates fu

This timely and synoptic text contains the essentials of queueing networks, from the classical product-form theory to the more recent developments such as diffusion and fluid limits, stochastic compa

This book deals with the study of a class of stochastic differential systems having unbounded coefficients, both in finite and in infinite dimension. The attention is focused on the regularity propert

At first there was the Markov property. The theory of stochastic processes, which can be considered as an exten- sion of probability theory, allows the modeling of the evolution of systems through the

"Written by two renowned experts in the field, the books under review contain a thorough and insightful treatment of the fundamental underpinnings of various aspects of stochastic processes as well as

This book, first published in 2000, addresses problems in financial mathematics of pricing and hedging derivative securities in an environment of uncertain and changing market volatility. These problems are important to investors from large trading institutions to pension funds. It presents mathematical and statistical tools that exploit the bursty nature of market volatility. The mathematics is introduced through examples and illustrated with simulations and the modeling approach that is described is validated and tested on market data. The material is suitable for a one semester course for graduate students who have had exposure to methods of stochastic modeling and arbitrage pricing theory in finance. It is easily accessible to derivatives practitioners in the financial engineering industry.

Have you ever used a stochastic oscillator?Does your portfolio have spiders in it?Do you really know what a derivative is?From the creators of one of today’s most popular investing Web sites,The Inves

The Second Edition of INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS focuses on developing the skills to build probability (stochastic) models. Lee J. Bain and Max Engelhardt focus on the ma

Lévy processes are rich mathematical objects and constitute perhaps the most basic class of stochastic processes with a continuous time parameter. This book is intended to provide the reader with comprehensive basic knowledge of Lévy processes, and at the same time serve as an introduction to stochastic processes in general. No specialist knowledge is assumed and proofs are given in detail. Systematic study is made of stable and semi-stable processes, and the author gives special emphasis to the correspondence between Lévy processes and infinitely divisible distributions. All serious students of random phenomena will find that this book has much to offer.

Great advances have been made in recent years in the field of computational probability. In particular, the state of the art - as it relates to queuing systems, stochastic Petri-nets and systems de

This book provides an up-to-date introduction to current research in fluctuations in spatially extended systems. It offers a practical introduction to the theory of stochastic partial differential equ

Authoritative coverage of a revolutionary technique for overcoming problems in electromagnetic design Genetic algorithms are stochastic search procedures modeled on the Darwinian concepts of natural s

Signal analysis gives an insight into the properties of signals and stochastic processes by methodology. Linear transforms are integral to the continuing growth of signal processes as they characteriz

Asymptotic methods are of great importance for practical applications, especially in dealing with boundary value problems for small stochastic perturbations. This book deals with nonlinear dynamical s

The first systematic presentation of the theory of dynamical systems under the influence of randomness, this book includes products of random mappings as well as random and stochastic differential equ

The first systematic presentation of the theory of dynamical systems under the influence of randomness, this book includes products of random mappings as well as random and stochastic differential equ

This volume is devoted to the investigation of general Borel models of stochastic optimal control, taking into consideration additional performance criteria which must satisfy the constraints-inequ

This book is devoted to the asymptotic properties of solutions of stochastic evolution equations in infinite dimensional spaces. It is divided into three parts: Markovian dynamical systems; invariant measures for stochastic evolution equations; invariant measures for specific models. The focus is on models of dynamical processes affected by white noise, which are described by partial differential equations such as the reaction-diffusion equations or Navier–Stokes equations. Besides existence and uniqueness questions, special attention is paid to the asymptotic behaviour of the solutions, to invariant measures and ergodicity. Some of the results found here are presented for the first time. For all whose research interests involve stochastic modelling, dynamical systems, or ergodic theory, this book will be an essential purchase.

This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part one reviews stochastic convergence in its various forms. Part two off

An Introduction to Statistical Signal Processing with Applications covers basic techniques in the processing of stochastic signals and illustrate their use in a variety of specific applications. Th

Covering deterministic scheduling, stochastic scheduling, and the probabilistic analysis of algorithms, this unusually broad view of the subject brings together tutorials, surveys and articles with or

"Engaging, elegantly written." — Applied Mathematical Modelling. A distinguished theoretical chemist and engineer discusses the types of models — finite, statistical, stochastic, and more — as well as

Explains how Hilbert space techniques cross the boundaries into the foundations of probability and statistics. Focuses on the theory of martingales stochastic integration, interpolation and density es

This book provides an easily accessible, computationally-oriented introduction into the numerical solution of stochastic differential equations using computer experiments. It develops in the reader an

Modern survival analysis and more general event history analysis may be effectively handled in the mathematical framework of counting processes, stochastic integration, martingale central limit theor