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Author: Ronald A. Howard
Publisher: Courier Corporation
ISBN: 0486140679
Category : Mathematics
Languages : en
Pages : 610
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Book Description
This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. Its intent is to equip readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics and space engineering to marketing. More than a collection of techniques, it constitutes a guide to the consistent application of the fundamental principles of probability and linear system theory. Author Ronald A. Howard, Professor of Management Science and Engineering at Stanford University, begins with the basic Markov model, proceeding to systems analyses of linear processes and Markov processes, transient Markov processes and Markov process statistics, and statistics and inference. Subsequent chapters explore recurrent events and random walks, Markovian population models, and time-varying Markov processes. Volume I concludes with a pair of helpful indexes.
Author: Ronald A. Howard
Publisher: Courier Corporation
ISBN: 0486140679
Category : Mathematics
Languages : en
Pages : 610
Get Book Here
Book Description
This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. Its intent is to equip readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics and space engineering to marketing. More than a collection of techniques, it constitutes a guide to the consistent application of the fundamental principles of probability and linear system theory. Author Ronald A. Howard, Professor of Management Science and Engineering at Stanford University, begins with the basic Markov model, proceeding to systems analyses of linear processes and Markov processes, transient Markov processes and Markov process statistics, and statistics and inference. Subsequent chapters explore recurrent events and random walks, Markovian population models, and time-varying Markov processes. Volume I concludes with a pair of helpful indexes.
Author: Ronald A. Howard
Publisher: Courier Corporation
ISBN: 0486152006
Category : Mathematics
Languages : en
Pages : 857
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Book Description
This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. Its intent is to equip readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics and space engineering to marketing. More than a collection of techniques, it constitutes a guide to the consistent application of the fundamental principles of probability and linear system theory. Author Ronald A. Howard, Professor of Management Science and Engineering at Stanford University, continues his treatment from Volume I with surveys of the discrete- and continuous-time semi-Markov processes, continuous-time Markov processes, and the optimization procedure of dynamic programming. The final chapter reviews the preceding material, focusing on the decision processes with discussions of decision structure, value and policy iteration, and examples of infinite duration and transient processes. Volume II concludes with an appendix listing the properties of congruent matrix multiplication.
Author: Adrian Gheorghe
Publisher: Springer Science & Business Media
ISBN: 9780792305446
Category : Mathematics
Languages : en
Pages : 382
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Book Description
'Et moi *...* si j'avait su comment en revenir. One service mathematics has rendered the je n'y serais point aile: human race. It has put common sense back where it belongs. on the topmost shelf next Jules Verne (0 the dusty canister labelled 'discarded non sense'. The series is divergent; therefore we may be able to do something with it. Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.
Author: Bin Wu
Publisher: Academic Press
ISBN: 0124077390
Category : Technology & Engineering
Languages : en
Pages : 225
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Book Description
Featuring aerospace examples and applications, Reliability Analysis of Dynamic Systems presents the very latest probabilistic techniques for accurate and efficient dynamic system reliability analysis. While other books cover more broadly the reliability techniques and challenges related to large systems, Dr Bin Wu presents a focused discussion of new methods particularly relevant to the reliability analysis of large aerospace systems under harmonic loads in the low frequency range. Developed and written to help you respond to challenges such as non-linearity of the failure surface, intensive computational costs and complexity in your dynamic system, Reliability Analysis of Dynamic Systems is a specific, detailed and application-focused reference for engineers, researchers and graduate students looking for the latest modeling solutions. The Shanghai Jiao Tong University Press Aerospace Series publishes titles that cover the latest advances in research and development in aerospace. Its scope includes theoretical studies, design methods, and real-world implementations and applications. The readership for the series is broad, reflecting the wide range of aerospace interest and application, but focuses on engineering. Forthcoming titles in the Shanghai Jiao Tong University Press Aerospace Series: Reliability Analysis of Dynamic Systems • Wake Vortex Control • Aeroacoustics: Fundamentals and Applications in Aeropropulsion Systems • Computational Intelligence in Aerospace Design • Unsteady Flow and Aeroelasticity in Turbomachinery - Authored by a leading figure in Chinese aerospace with 20 years' professional experience in reliability analysis and engineering simulation. - Offers solutions to the challenges of non-linearity, intensive computational cost and complexity in reliability assessment. - Aerospace applications and examples used throughout to illustrate accuracy and efficiency achieved with new methods.
Author: E.G. Frankel
Publisher: Springer Science & Business Media
ISBN: 9400927762
Category : Technology & Engineering
Languages : en
Pages : 443
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Book Description
Ernst G. Frankel This book has its origin in lecture notes developed over several years for use in a course in Systems Reliability f~r engineers concerned with the design of physical systems such as civil structures, power plants, and transport systems of all types. Increasing public concern with the reliability of systems for reasons of human safety, environmental protection, and acceptable investment risk limitations has resulted in an increasing interest by engineers in the formal application of reliability theory to engineering design. At the same time there is a demand for more effective approaches to the design of procedures for the operation and use of man made systems, more meaningful assessment of the risks introduced, and use such a system poses both when operating as designed and when operating at below design performance. The purpose of the book is to provide a sound, yet practical, introduction to reliability analysis and risk assessment which can be used by professionals in engineering, planning, management, and economics to improve the design, operation, and risk assessment of systems of interest. The text should be useful for students in many disciplines and is designed for fourth-year undergraduates or first-year graduate students. I would like to acknowledge the help of many of my graduate students who contributed to the development of this book by offering comments and criticism. Similarly, I would like to thank Mrs. Sheila McNary who typed untold drafts of the manuscript, and Mr.
Author: Avi Pfeffer
Publisher: Simon and Schuster
ISBN: 1638352372
Category : Computers
Languages : en
Pages : 650
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Book Description
Summary Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you'll learn how to use the PP paradigm to model application domains and then express those probabilistic models in code. Although PP can seem abstract, in this book you'll immediately work on practical examples, like using the Figaro language to build a spam filter and applying Bayesian and Markov networks, to diagnose computer system data problems and recover digital images. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The data you accumulate about your customers, products, and website users can help you not only to interpret your past, it can also help you predict your future! Probabilistic programming uses code to draw probabilistic inferences from data. By applying specialized algorithms, your programs assign degrees of probability to conclusions. This means you can forecast future events like sales trends, computer system failures, experimental outcomes, and many other critical concerns. About the Book Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In this book, you’ll immediately work on practical examples like building a spam filter, diagnosing computer system data problems, and recovering digital images. You’ll discover probabilistic inference, where algorithms help make extended predictions about issues like social media usage. Along the way, you’ll learn to use functional-style programming for text analysis, object-oriented models to predict social phenomena like the spread of tweets, and open universe models to gauge real-life social media usage. The book also has chapters on how probabilistic models can help in decision making and modeling of dynamic systems. What's Inside Introduction to probabilistic modeling Writing probabilistic programs in Figaro Building Bayesian networks Predicting product lifecycles Decision-making algorithms About the Reader This book assumes no prior exposure to probabilistic programming. Knowledge of Scala is helpful. About the Author Avi Pfeffer is the principal developer of the Figaro language for probabilistic programming. Table of Contents PART 1 INTRODUCING PROBABILISTIC PROGRAMMING AND FIGARO Probabilistic programming in a nutshell A quick Figaro tutorial Creating a probabilistic programming application PART 2 WRITING PROBABILISTIC PROGRAMS Probabilistic models and probabilistic programs Modeling dependencies with Bayesian and Markov networks Using Scala and Figaro collections to build up models Object-oriented probabilistic modeling Modeling dynamic systems PART 3 INFERENCE The three rules of probabilistic inference Factored inference algorithms Sampling algorithms Solving other inference tasks Dynamic reasoning and parameter learning
Author: Ed Brinksma
Publisher: Springer
ISBN: 3540446672
Category : Computers
Languages : en
Pages : 438
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Book Description
Traditionally, models and methods for the analysis of the functional correctness of reactive systems, and those for the analysis of their performance (and - pendability) aspects, have been studied by di?erent research communities. This has resulted in the development of successful, but distinct and largely unrelated modeling and analysis techniques for both domains. In many modern systems, however, the di?erence between their functional features and their performance properties has become blurred, as relevant functionalities become inextricably linked to performance aspects, e.g. isochronous data transfer for live video tra- mission. During the last decade, this trend has motivated an increased interest in c- bining insights and results from the ?eld of formal methods – traditionally - cused on functionality – with techniques for performance modeling and analysis. Prominent examples of this cross-fertilization are extensions of process algebra and Petri nets that allow for the automatic generation of performance models, the use of formal proof techniques to assess the correctness of randomized - gorithms, and extensions of model checking techniques to analyze performance requirements automatically. We believe that these developments markthe - ginning of a new paradigm for the modeling and analysis of systems in which qualitative and quantitative aspects are studied from an integrated perspective. We are convinced that the further worktowards the realization of this goal will be a growing source of inspiration and progress for both communities.
Author: A. Mital
Publisher: Springer Science & Business Media
ISBN: 9401107033
Category : Science
Languages : en
Pages : 412
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Book Description
This book is aimed at both researchers and practitioners, and provides a collection of expert systems in manufacturing and production engineering along with their knowledge base and rules. We believe that inclusion of the knowledge base and associated rules is essential if practitioners are to derive full benefit from these expert systems. This unique book is the result of our belief and the efforts of our distinguished colleagues who subscribe to this philosophy. A total of 15 different expert systems are included in this book. These expert systems are preceded by an introductory chapter written by Kuo, Preface XVll Mital and Anand. The expert system rules are included on a floppy disk in ASCII and can be easily accessed. These rules and the description of the expert system's structure should assist the users in customizing these systems. Overall, the expert systems included in this volume cover a fairly wide variety of manufacturing and production engineering topics.
Author: Oliver Ibe
Publisher: Newnes
ISBN: 0124078397
Category : Mathematics
Languages : en
Pages : 515
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Book Description
Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. - Presents both the theory and applications of the different aspects of Markov processes - Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented - Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.
Author: P. R. Kumar
Publisher: SIAM
ISBN: 1611974259
Category : Mathematics
Languages : en
Pages : 371
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Book Description
Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
