Operations research uses quantitative models to analyze and predict the behavior of systems and to provide information for decision makers. Two key concepts in such research are optimization and uncertainty. Typical models in stochastic operations research include queueing models, inventory models, financial engineering models, reliability models, and simulation models. This book contains a collection of peer-reviewed papers from the International Workshop on Recent Advances in Stochastic Operations Research (2007 RASOR Nanzan) held on March 5-6, 2007, at Nanzan University, Nagoya, Japan. It enables advanced readers to understand the recent topics and results in stochastic operations research.

This book provides, as simply as possible, sound foundations for an in-depth understanding of reliability engineering with regard to qualitative analysis, modelling, and probabilistic calculations of safety and production systems. Drawing on the authors' extensive experience within the field of reliability engineering, it addresses and discusses a variety of topics, including: * Background and overview of safety and dependability studies; * Explanation and critical analysis of definitions related to core concepts; * Risk identification through qualitative approaches (preliminary hazard analysis, HAZOP, FMECA, etc.); * Modelling of industrial systems through static (fault tree, reliability block diagram), sequential (cause-consequence diagrams, event trees, LOPA, bowtie), and dynamic (Markov graphs, Petri nets) approaches; * Probabilistic calculations through state-of-the-art analytical or Monte Carlo simulation techniques; * Analysis, modelling, and calculations of common cause failure and uncertainties; * Linkages and combinations between the various modelling and calculation approaches; * Reliability data collection and standardization. The book features illustrations, explanations, examples, and exercises to help readers gain a detailed understanding of the topic and implement it into their own work. Further, it analyses the production availability of production systems and the functional safety of safety systems (SIL calculations), showcasing specific applications of the general theory discussed. Given its scope, this book is a valuable resource for engineers, software designers, standard developers, professors, and students.

The Malliavin calculus (or stochastic calculus of variations) is an infinite-dimensional differential calculus on a Gaussian space. Originally, it was developed to provide a probabilistic proof to HArmander's "sum of squares" theorem, but it has found a wide range of applications in stochastic analysis. This monograph presents the main features of the Malliavin calculus and discusses in detail its main applications. The author begins by developing the analysis on the Wiener space, and then uses this to establish the regularity of probability laws and to prove HArmander's theorem. The regularity of the law of stochastic partial differential equations driven by a space-time white noise is also studied. The subsequent chapters develop the connection of the Malliavin with the anticipating stochastic calculus, studying anticipating stochastic differential equations and the Markov property of solutions to stochastic differential equations with boundary conditions.

The second edition of this monograph includes recent applications of the Malliavin calculus in finance and a chapter devoted to the stochastic calculus with respect to the fractional Brownian motion.

Since the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, Introduction to Stochastic Calculus Applied to Finance, Second Edition incorporates some of these new techniques and concepts to provide an accessible, up-to-date initiation to the field. New to the Second Edition Complements on discrete models, including Rogers' approach to the fundamental theorem of asset pricing and super-replication in incomplete markets Discussions on local volatility, Dupire's formula, the change of numeraire techniques, forward measures, and the forward Libor model A new chapter on credit risk modeling An extension of the chapter on simulation with numerical experiments that illustrate variance reduction techniques and hedging strategies Additional exercises and problems Providing all of the necessary stochastic calculus theory, the authors cover many key finance topics, including martingales, arbitrage, option pricing, American and European options, the Black-Scholes model, optimal hedging, and the computer simulation of financial models. They succeed in producing a solid introduction to stochastic approaches used in the financial world.

Stochastic Process Optimization using Aspen (R) Plus Bookshop Category: Chemical Engineering Optimization can be simply defined as "choosing the best alternative among a set of feasible options". In all the engineering areas, optimization has a wide range of applications, due to the high number of decisions involved in an engineering environment. Chemical engineering, and particularly process engineering, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the combined use of the modular simulator Aspen (R) Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts of optimization are first presented, then, strategies to use the simulator linked with the optimization algorithm are shown. Finally, examples of application for process engineering are discussed. The reader will learn how to link the process simulator Aspen (R) Plus and stochastic optimization algorithms to solve process design problems. They will gain ability to perform multi-objective optimization in several case studies. Key Features: * The book links simulation and optimization through numerical analyses and stochastic optimization techniques * Includes use of examples to illustrate the application of the concepts and specific guidance on the use of software (Aspen (R) Plus, Excel, MATLB) to set up and solve models representing complex problems. * Illustrates several examples of applications for the linking of simulation and optimization software with other packages for optimization purposes. * Provides specific information on how to implement stochastic optimization with process simulators. * Enable readers to identify practical and economic solutions to problems of industrial relevance, enhancing the safety, operation, environmental, and economic performance of chemical processes.

The first book to examine weakly stationary random fields and their connections with invariant subspaces (an area associated with functional analysis). It reviews current literature, presents central issues and most important results within the area. For advanced Ph.D. students, researchers, especially those conducting research on Gaussian theory.

This volume features a collection of contributed articles and lecture notes from the XI Symposium on Probability and Stochastic Processes, held at CIMAT Mexico in September 2013. Since the symposium was part of the activities organized in Mexico to celebrate the International Year of Statistics, the program included topics from the interface between statistics and stochastic processes.

Stochastic differential equations in infinite dimensional spaces are motivated by the theory and analysis of stochastic processes and by applications such as stochastic control, population biology, and turbulence, where the analysis and control of such systems involves investigating their stability. While the theory of such equations is well established, the study of their stability properties has grown rapidly only in the past 20 years, and most results have remained scattered in journals and conference proceedings. This book offers a systematic presentation of the modern theory of the stability of stochastic differential equations in infinite dimensional spaces - particularly Hilbert spaces. The treatment includes a review of basic concepts and investigation of the stability theory of linear and nonlinear stochastic differential equations and stochastic functional differential equations in infinite dimensions. The final chapter explores topics and applications such as stochastic optimal control and feedback stabilization, stochastic reaction-diffusion, Navier-Stokes equations, and stochastic population dynamics. In recent years, this area of study has become the focus of increasing attention, and the relevant literature has expanded greatly. Stability of Infinite Dimensional Stochastic Differential Equations with Applications makes up-to-date material in this important field accessible even to newcomers and lays the foundation for future advances.

This book describes a completely novel mathematical development which has already influenced probability theory, and has potential for application to engineering and to areas of pure mathematics.

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. The use of simulation, by means of the popular statistical software R, makes theoretical results come alive with practical, hands-on demonstrations. Written by a highly-qualified expert in the field, the author presents numerous examples from a wide array of disciplines, which are used to illustrate concepts and highlight computational and theoretical results. Developing readers problem-solving skills and mathematical maturity, Introduction to Stochastic Processes with R features: * More than 200 examples and 600 end-of-chapter exercises * A tutorial for getting started with R, and appendices that contain review material in probability and matrix algebra * Discussions of many timely and stimulating topics including Markov chain Monte Carlo, random walk on graphs, card shuffling, Black Scholes options pricing, applications in biology and genetics, cryptography, martingales, and stochastic calculus * Introductions to mathematics as needed in order to suit readers at many mathematical levels * A companion web site that includes relevant data files as well as all R code and scripts used throughout the book Introduction to Stochastic Processes with R is an ideal textbook for an introductory course in stochastic processes. The book is aimed at undergraduate and beginning graduate-level students in the science, technology, engineering, and mathematics disciplines. The book is also an excellent reference for applied mathematicians and statisticians who are interested in a review of the topic.

Infinite Divisibility of Probability Distributions on the Real Line reassesses classical theory and presents new developments, while focusing on divisibility with respect to convolution or addition of independent random variables. This definitive, example-rich text supplies approximately 100 examples to correspond with all major chapter topics and reviews infinite divisibility in light of the central limit problem. It contrasts infinite divisibility with finite divisibility, discusses the preservation of infinite divisibility under mixing for many classes of distributions, and investigates self-decomposability and stability on the nonnegative reals, nonnegative integers, and the reals.

The investigation of the origin and formation of microstructures and the effect that microstructure has on the properties of materials are important issues in materials science and technology. Geometrical analysis is often the key to understanding the formation of microstructures and the resulting material properties. The authors make use of mathematical morphology, spatial statistics, image processing, stereology and stochastic geometry to analyze microstructures arising in materials science.

• Quantitative microstructure analysis is one of the most successful experimental techniques in materials science

• Uses examples to demonstrate the techniques

• Program code included enables the reader to apply the numerous algorithms

• Accessible to material scientists with limited statistical knowledge

Game theory involves multi-person decision making and differential dynamic game theory has been widely applied to n-person decision making problems, which are stimulated by a vast number of applications. This book addresses the gap to discuss general stochastic n-person noncooperative and cooperative game theory with wide applications to control systems, signal processing systems, communication systems, managements, financial systems, and biological systems. H game strategy, n-person cooperative and noncooperative game strategy are discussed for linear and nonlinear stochastic systems along with some computational algorithms developed to efficiently solve these game strategies.

This book will cover heuristic optimization techniques and applications in engineering problems. The book will be divided into three sections that will provide coverage of the techniques, which can be employed by engineers, researchers, and manufacturing industries, to improve their productivity with the sole motive of socio-economic development. This will be the first book in the category of heuristic techniques with relevance to engineering problems and achieving optimal solutions. Features Explains the concept of optimization and the relevance of using heuristic techniques for optimal solutions in engineering problems Illustrates the various heuristics techniques Describes evolutionary heuristic techniques like genetic algorithm and particle swarm optimization Contains natural based techniques like ant colony optimization, bee algorithm, firefly optimization, and cuckoo search Offers sample problems and their optimization, using various heuristic techniques

This textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments -- with indications as to why a particular result holds, and also how it is connected with other results -- and illustrated by examples. Wherever possible, the book includes references to more specialised texts containing both proofs and more advanced material related to the topics covered.

Contents:
Preface 1. Notations and Some Technical Results 2. Random Sets 3. Random Probability Measures and the Narrow Topology 4. Prohorov Theory for Random Probability Measures 5. Further Topologies on Random Measures 6. Invariant Measures and Some Ergodic theory for Random Dynamical Systems A. The Narrow Topology on Non-Random Measures B. Scattered Results Bibliography Index

This book presents new research in probability theory using ideas from mathematical logic. It is a general study of stochastic processes on adapted probability spaces, employing the concept of similarity of stochastic processes based on the notion of adapted distribution. The authors use ideas from model theory and methods from nonstandard analysis. The construction of spaces with certain richness properties, defined by insights from model theory, becomes easy using nonstandard methods, but remains difficult or impossible without them.

This book aims to widen the understanding of stochastic dynamic choice and equilibrium models. It offers a simplified and heuristic exposition of the theory of Brownian motion and its control or regulation, rendering such methods more accessible to economists who do not require a detailed, mathematical treatment of the subject.
The main mathematical ideas are presented in a context which with which economists will be familiar. Using a binomial approach to Brownian motion, the mathematics is reduced to simple algebra, progressing to some equally simple limits. The starting point of the calculus of Brownian motion - 'Ito's Lemma' - emerges by analogy with the economics of risk-aversion. Conditions for the optimal regulation of Brownian motion, including the important, but often mysterious, 'smooth pasting' condition, are derived in a similar way. Each theoretical derivation is illustrated by developing a significant economic application, drawn mainly from recent research in macroeconomics and international economics.

Recognized as a "Recommended" title by Choice for their April 2021 issue. Choice is a publishing unit at the Association of College & Research Libraries (ACR&L), a division of the American Library Association. Choice has been the acknowledged leader in the provision of objective, high-quality evaluations of nonfiction academic writing. Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. This is usually applied when two or more objectives are to be optimized simultaneously. This book is presented with two major objectives. Firstly, it features chapters by eminent researchers in the field providing the readers about the current status of the subject. Secondly, algorithm-based optimization or advanced optimization techniques, which are applied to mostly non-engineering problems, are applied to engineering problems. This book will also serve as an aid to both research and industry. Usage of these methodologies would enable the improvement in engineering and manufacturing technology and support an organization in this era of low product life cycle. Features: Covers the application of recent and new algorithms Focuses on the development aspects such as including surrogate modeling, parallelization, game theory, and hybridization Presents the advances of engineering applications for both single-objective and multi-objective optimization problems Offers recent developments from a variety of engineering fields Discusses Optimization using Evolutionary Algorithms and Metaheuristics applications in engineering

This new edition of a successful, bestselling book continues to provide you with practical information on the use of statistical methods for solving real-world problems in complex industrial environments. Complete with examples from the chemical and pharmaceutical laboratory and manufacturing areas, this thoroughly updated book clearly demonstrates how to obtain reliable results by choosing the most appropriate experimental design and data evaluation methods.

Unlike other books on the subject, Statistical Methods in Analytical Chemistry, Second Edition presents and solves problems in the context of a comprehensive decision-making process under GMP rules: Would you recommend the destruction of a $100,000 batch of product if one of four repeat determinations barely fails the specification limit? How would you prevent this from happening in the first place? Are you sure the calculator you are using is telling the truth? To help you control these situations, the new edition:

• Covers univariate, bivariate, and multivariate data
• Features case studies from the pharmaceutical and chemical industries demonstrating typical problems analysts encounter and the techniques used to solve them
• Offers information on ancillary techniques, including a short introduction to optimization, exploratory data analysis, smoothing and computer simulation, and recapitulation of error propagation
• Boasts numerous Excel files and compiled Visual Basic programs–no statistical table lookups required!
• Uses Monte Carlo simulation to illustrate the variability inherent in statistically indistinguishable data sets

Statistical Methods in Analytical Chemistry, Second Edition is an excellent, one-of-a-kind resource for laboratory scientists and engineers and project managers who need to assess data reliability; QC staff, regulators, and customers who want to frame realistic requirements and specifications; as well as educators looking for real-life experiments and advanced students in chemistry and pharmaceutical science.

From the reviews of Statistical Methods in Analytical Chemistry, First Edition:

"This book is extremely valuable. The authors supply many very useful programs along with their source code. Thus, the user can check the authenticity of the result and gain a greater understanding of the algorithm from the code. It should be on the bookshelf of every analytical chemist. "—Applied Spectroscopy

"The authors have compiled an interesting collection of data to illustrate the application of statistical methods . . . including calibrating, setting detection limits, analyzing ANOVA data, analyzing stability data, and determining the influence of error propagation." —Clinical Chemistry

"The examples are taken from a chemical/pharmaceutical environment, but serve as convenient vehicles for the discussion of when to use which test, and how to make sense out of the results. While practical use of statistics is the major concern, it is put into perspective, and the reader is urged to use plausibility checks."& mdash;Journal of Chemical Education

"The discussion of univariate statistical tests is one of the more thorough I have seen in this type of book.... The treatment of linear regression is also thorough, and a complete set of equations for uncertainty in the results is presented.... The bibliography is extensive and will serve as a valuable resource for those seeking more information on virtually any topic covered in the book."—Journal of American Chemical Society

This book treats the application of statistics to analytical chemistry in a very practical manner. [It] integrates PC computing power, testing programs, and analytical know-how in the context of good manufacturing practice/good laboratory practice (GMP/GLP).... The book is of value in many fields of analytical chemistry and should be available in all relevant libraries." —Chemometrics and Intelligent Laboratory Systems

This book provides a rigorous yet accessible introduction to the theory of stochastic processes. A significant part of the book is devoted to the classic theory of stochastic processes. In turn, it also presents proofs of well-known results, sometimes together with new approaches. Moreover, the book explores topics not previously covered elsewhere, such as distributions of functionals of diffusions stopped at different random times, the Brownian local time, diffusions with jumps, and an invariance principle for random walks and local times. Supported by carefully selected material, the book showcases a wealth of examples that demonstrate how to solve concrete problems by applying theoretical results. It addresses a broad range of applications, focusing on concrete computational techniques rather than on abstract theory. The content presented here is largely self-contained, making it suitable for researchers and graduate students alike.

A path-breaking account of Markov decision processes-theory and computation
This book's clear presentation of theory, numerous chapter-end problems, and development of a unified method for the computation of optimal policies in both discrete and continuous time make it an excellent course text for graduate students and advanced undergraduates. Its comprehensive coverage of important recent advances in stochastic dynamic programming makes it a valuable working resource for operations research professionals, management scientists, engineers, and others.
Stochastic Dynamic Programming and the Control of Queueing Systems presents the theory of optimization under the finite horizon, infinite horizon discounted, and average cost criteria. It then shows how optimal rules of operation (policies) for each criterion may be numerically determined. A great wealth of examples from the application area of the control of queueing systems is presented. Nine numerical programs for the computation of optimal policies are fully explicated.
The Pascal source code for the programs is available for viewing and downloading on the Wiley Web site at www.wiley.com/products/subject/mathematics. The site contains a link to the author's own Web site and is also a place where readers may discuss developments on the programs or other aspects of the material. The source files are also available via ftp at ftp: //ftp.wiley.com/public/sci_tech_med/stochastic
Stochastic Dynamic Programming and the Control of Queueing Systems features:
* Path-breaking advances in Markov decision process techniques, brought together for the first time in book form
* A theorem/proof format (proofs may be omittedwithout loss of continuity)
* Development of a unified method for the computation of optimal rules of system operation
* Numerous examples drawn mainly from the control of queueing systems
* Detailed discussions of nine numerical programs
* Helpful chapter-end problems
* Appendices with complete treatment of background material

The monograph addresses a problem of stochastic analysis based on the uncertainty assessment by simulation and application of this method in ecology and steel industry under uncertainty. The first chapter defines the Monte Carlo (MC) method and random variables in stochastic models. Chapter two deals with the contamination transport in porous media. Stochastic approach for Municipal Solid Waste transit time contaminants modeling using MC simulation has been worked out. The third chapter describes the risk analysis of the waste to energy facility proposal for Konin city, including the financial aspects. Environmental impact assessment of the ArcelorMittal Steel Power Plant, in Krakow - in the chapter four - is given. Thus, four scenarios of the energy mix production processes were studied. Chapter five contains examples of using ecological Life Cycle Assessment (LCA) - a relatively new method of environmental impact assessment - which help in preparing pro-ecological strategy, and which can lead to reducing the amount of wastes produced in the ArcelorMittal Steel Plant production processes. Moreover, real input and output data of selected processes under uncertainty, mainly used in the LCA technique, have been examined. The last chapter of this monograph contains final summary. The log-normal probability distribution, widely used in risk analysis and environmental management, in order to develop a stochastic analysis of the LCA, as well as uniform distribution for stochastic approach of pollution transport in porous media has been proposed. The distributions employed in this monograph are assembled from site-specific data, data existing in the most current literature, and professional judgment."

Complex stochastic systems comprises a vast area of research, from modelling specific applications to model fitting, estimation procedures, and computing issues. The exponential growth in computing power over the last two decades has revolutionized statistical analysis and led to rapid developments and great progress in this emerging field. In Complex Stochastic Systems, leading researchers address various statistical aspects of the field, illustrated by some very concrete applications.

A Primer on Markov Chain Monte Carlo by Peter J. Green provides a wide-ranging mixture of the mathematical and statistical ideas, enriched with concrete examples and more than 100 references.
Causal Inference from Graphical Models by Steffen L. Lauritzen explores causal concepts in connection with modelling complex stochastic systems, with focus on the effect of interventions in a given system.
State Space and Hidden Markov Models by Hans R. Künschshows the variety of applications of this concept to time series in engineering, biology, finance, and geophysics.
Monte Carlo Methods on Genetic Structures by Elizabeth A. Thompson investigates special complex systems and gives a concise introduction to the relevant biological methodology.
Renormalization of Interacting Diffusions by Frank den Hollander presents recent results on the large space-time behavior of infinite systems of interacting diffusions.
Stein's Method for Epidemic Processes by Gesine Reinert investigates the mean field behavior of a general stochastic epidemic with explicit bounds.

Individually, these articles provide authoritative, tutorial-style exposition and recent results from various subjects related to complex stochastic systems. Collectively, they link these separate areas of study to form the first comprehensive overview of this rapidly developing field.

Stochastic Processes for Insurance and Finance offers a thorough yet accessible reference for researchers and practitioners of insurance mathematics. Building on recent and rapid developments in applied probability the authors describe in general terms models based on Markov processes, martingales and various types of point processes. Discussing frequently asked insurance questions, the authors present a coherent overview of the subject and specifically address:

• the principal concepts of insurance and finance
• practical examples with real life data
• numerical and algorithmic procedures essential for modern insurance practices