Probabilistic Modeling in System Engineering PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Probabilistic Modeling in System Engineering PDF full book. Access full book title Probabilistic Modeling in System Engineering by Andrey Kostogryzov. Download full books in PDF and EPUB format.
Author: Andrey Kostogryzov
Publisher: BoD – Books on Demand
ISBN: 1789237742
Category : Technology & Engineering
Languages : en
Pages : 292
Get Book Here
Book Description
This book is intended for systems analysts, designers, developers, users, experts, as well as those involved in quality, risk, safety and security management, and, of course, scientists and students. The various sets of original and traditional probabilistic models and interesting results of their applications to the research of different systems are presented. The models are understandable and applicable for solving system engineering problems: to optimize system requirements, compare different processes, rationale technical decisions, carry out tests, adjust technological parameters, and predict and analyze quality and risks. The engineering decisions, scientifically proven by the proposed models and software tools, can provide purposeful, essential improvement of quality and mitigation of risks, and reduce the expense of operating systems. Models, methods, and software tools can also be used in education for system analysis and mathematical modeling on specializations, for example "systems engineering," "operations research," "enterprise management," "project management," "risk management," "quality of systems," "safety and security," "smart systems," "system of systems," etc.
Author: Andrey Kostogryzov
Publisher: BoD – Books on Demand
ISBN: 1789237742
Category : Technology & Engineering
Languages : en
Pages : 292
Get Book Here
Book Description
This book is intended for systems analysts, designers, developers, users, experts, as well as those involved in quality, risk, safety and security management, and, of course, scientists and students. The various sets of original and traditional probabilistic models and interesting results of their applications to the research of different systems are presented. The models are understandable and applicable for solving system engineering problems: to optimize system requirements, compare different processes, rationale technical decisions, carry out tests, adjust technological parameters, and predict and analyze quality and risks. The engineering decisions, scientifically proven by the proposed models and software tools, can provide purposeful, essential improvement of quality and mitigation of risks, and reduce the expense of operating systems. Models, methods, and software tools can also be used in education for system analysis and mathematical modeling on specializations, for example "systems engineering," "operations research," "enterprise management," "project management," "risk management," "quality of systems," "safety and security," "smart systems," "system of systems," etc.
Author: Pijush Samui
Publisher: Butterworth-Heinemann
ISBN: 0128165464
Category : Computers
Languages : en
Pages : 592
Get Book Here
Book Description
Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems
Author: Haym Benaroya
Publisher: CRC Press
ISBN: 9780824723156
Category : Science
Languages : en
Pages : 770
Get Book Here
Book Description
Certainty exists only in idealized models. Viewed as the quantification of uncertainties, probabilitry and random processes play a significant role in modern engineering, particularly in areas such as structural dynamics. Unlike this book, however, few texts develop applied probability in the practical manner appropriate for engineers. Probability Models in Engineering and Science provides a comprehensive, self-contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and while doing so, develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations and an engaging, human connection to the subject, achieved through short biographies of some of the key people in the field. End-of-chapter problems help solidify understanding and footnotes to the literature expand the discussions and introduce relevant journals and texts. This book builds the background today's engineers need to deal explicitly with the scatter observed in experimental data and with intricate dynamic behavior. Designed for undergraduate and graduate coursework as well as self-study, the text's coverage of theory, approximation methods, and numerical methods make it equally valuable to practitioners.
Author: Joel A. Nachlas
Publisher: CRC Press
ISBN: 1315307588
Category : Technology & Engineering
Languages : en
Pages : 378
Get Book Here
Book Description
Without proper reliability and maintenance planning, even the most efficient and seemingly cost-effective designs can incur enormous expenses due to repeated or catastrophic failure and subsequent search for the cause. Today’s engineering students face increasing pressure from employers, customers, and regulators to produce cost-efficient designs that are less prone to failure and that are safe and easy to use. The second edition of Reliability Engineering aims to provide an understanding of reliability principles and maintenance planning to help accomplish these goals. This edition expands the treatment of several topics while maintaining an integrated introductory resource for the study of reliability evaluation and maintenance planning. The focus across all of the topics treated is the use of analytical methods to support the design of dependable and efficient equipment and the planning for the servicing of that equipment. The argument is made that probability models provide an effective vehicle for portraying and evaluating the variability that is inherent in the performance and longevity of equipment. With a blend of mathematical rigor and readability, this book is the ideal introductory textbook for graduate students and a useful resource for practising engineers.
Author: Martin Pelikan
Publisher: Springer Science & Business Media
ISBN: 3540349537
Category : Mathematics
Languages : en
Pages : 363
Get Book Here
Book Description
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
Author: Carlo Pellegrino
Publisher: Springer Nature
ISBN: 3030918777
Category : Technology & Engineering
Languages : en
Pages : 1454
Get Book Here
Book Description
This book gathers the latest advances and innovations in the field of quality control and improvement of bridges and structures, as presented by international researchers and engineers at the 1st Conference of the European Association on Quality Control of Bridges and Structures (EUROSTRUCT 2021), held in Padua, Italy on August 29 – September 1, 2021. Contributions include a wide range of topics such as testing and advanced diagnostic techniques for damage detection; SHM and AI, IoT and machine learning for data analysis of bridges and structures; fiberoptics and smart sensors for long-term SHM; structural reliability, risk, robustness, redundancy and resilience for bridges; corrosion models, fatigue analysis and impact of hazards on infrastructure components; bridge and asset management systems, and decision-making models; Life-Cycle Analysis, retrofit and service-life extension, risk management protocols; quality control plans, sustainability and green materials.
Author: Andrey Kostogryzov
Publisher:
ISBN: 9781789844092
Category : Engineering (General). Civil engineering (General)
Languages : en
Pages : 290
Get Book Here
Book Description
This book is intended for systems analysts, designers, developers, users, experts, as well as those involved in quality, risk, safety and security management, and, of course, scientists and students. The various sets of original and traditional probabilistic models and interesting results of their applications to the research of different systems are presented. The models are understandable and applicable for solving system engineering problems: to optimize system requirements, compare different processes, rationale technical decisions, carry out tests, adjust technological parameters, and predict and analyze quality and risks. The engineering decisions, scientifically proven by the proposed models and software tools, can provide purposeful, essential improvement of quality and mitigation of risks, and reduce the expense of operating systems. Models, methods, and software tools can also be used in education for system analysis and mathematical modeling on specializations, for example ""systems engineering,"" ""operations research,"" ""enterprise management,"" ""project management,"" ""risk management,"" ""quality of systems,"" ""safety and security,"" ""smart systems,"" ""system of systems,"" etc.
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: Annabelle McIver
Publisher: Springer Science & Business Media
ISBN: 9780387401157
Category : Computers
Languages : en
Pages : 412
Get Book Here
Book Description
Provides an integrated coverage of random/probabilistic algorithms, assertion-based program reasoning, and refinement programming models, providing a focused survey on probabilistic program semantics. This book illustrates, by examples, the typical steps necessary to build a mathematical model of any programming paradigm.
Author: Daphne Koller
Publisher: MIT Press
ISBN: 0262258358
Category : Computers
Languages : en
Pages : 1270
Get Book Here
Book Description
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
