The Monte Carlo Simulation Method for System Reliability and Risk Analysis, Enrico Zio
Автор: Asmussen Название: Stochastic Simulation: Algorithms and Analysis ISBN: 038730679X ISBN-13(EAN): 9780387306797 Издательство: Springer Рейтинг: Цена: 6981.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods , as well as accompanying mathematical analysis of the convergence properties of the methods discussed . The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focusses on general methods, whereas the second half discusses model-specific algorithms. Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value. Soren Asmussen is Professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is Thomas Ford Professor of Engineering at Stanford University.
Название: Stochastic Simulation and Monte Carlo Methods ISBN: 3642393624 ISBN-13(EAN): 9783642393624 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes.
Автор: Nick T. Thomopoulos Название: Essentials of Monte Carlo Simulation ISBN: 1489986081 ISBN-13(EAN): 9781489986085 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. It illustrates the best ways to select input distributions and parameters with or without sample data.
Автор: Rubinstein Reuven Y. Название: Simulation and the Monte Carlo Method ISBN: 1118632168 ISBN-13(EAN): 9781118632161 Издательство: Wiley Рейтинг: Цена: 17416.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago.
Автор: Carlo Jacoboni; Paolo Lugli Название: The Monte Carlo Method for Semiconductor Device Simulation ISBN: 3211821104 ISBN-13(EAN): 9783211821107 Издательство: Springer Рейтинг: Цена: 28734.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume presents the application of the Monte Carlo method to the simulation of semiconductor devices, reviewing the physics of transport in semiconductors, followed by an introduction to the physics of semiconductor devices.
Описание: This practically oriented book presents a comprehensive, up-to-date description of multi-state system (MSS) reliability as a natural extension of classical binary-state reliability. It presents all essential theoretical achievements in the field.
Автор: Paolo Gardoni Название: Risk and Reliability Analysis: Theory and Applications ISBN: 3319524240 ISBN-13(EAN): 9783319524245 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Risk and Reliability Analysis.- Structural System Reliability, Reloaded.- Global Buckling Reliability Analysis of Slender Network Arch Bridges: An Application of Monte Carlo-based Estimation by Optimized Fitting.- Review of Quantitative Reliability Methods for Onshore Oil and Gas Pipelines.- An Intuitive Basis of the Probability Density Evolution Method (PDEM) for Stochastic Dynamics.- The Tail Equivalent Linearization Method for Nonlinear Stochastic Processes, Genesis and Developments.- Estimate of Small First Passage Probabilities of Nonlinear Random Vibration Systems.- Generation of Non-synchronous Earthquake Signals.- Seismic Response Analysis with Spatially Varying Stochastic Excitation.- Application of CQC Method to Seismic Response Control with Viscoelastic Dampers.
Описание: Multiobjective and Multicriteria Problems and Decision Models.- Multiobjective and Multicriteria Decision Processes and Methods.- Basic Concepts on Risk Analysis, Reliability and Maintenance.- Multidimensional Risk Analysis.- Preventive Maintenance Decisions.- Decision Making in Condition-Based Maintenance.- Decision on Maintenance Outsourcing.- Spare Parts Planning Decisions.- Decision on Redundancy Allocation.- Design Selection Decisions.- Decisions on Priority Assignment for Maintenance Planning.- Other Risk, Reliability and Maintenance Decision Problems.
Автор: Del Moral Название: Mean Field Simulation for Monte Carlo Integration ISBN: 1138198730 ISBN-13(EAN): 9781138198739 Издательство: Taylor&Francis Рейтинг: Цена: 7961.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to parallel and distributed computation, these advanced particle algorithms include nonlinear interacting jump diffusions; quantum, diffusion, and resampled Monte Carlo methods; Feynman-Kac particle models; genetic and evolutionary algorithms; sequential Monte Carlo methods; adaptive and interacting Markov chain Monte Carlo models; bootstrapping methods; ensemble Kalman filters; and interacting particle filters.
Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle simulation models and interdisciplinary research topics, including interacting jumps and McKean-Vlasov processes, sequential Monte Carlo methodologies, genetic particle algorithms, genealogical tree-based algorithms, and quantum and diffusion Monte Carlo methods.
Along with covering refined convergence analysis on nonlinear Markov chain models, the author discusses applications related to parameter estimation in hidden Markov chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty propagations in numerical codes, rare event simulation, financial mathematics, and free energy and quasi-invariant measures arising in computational physics and population biology.
This book shows how mean field particle simulation has revolutionized the field of Monte Carlo integration and stochastic algorithms. It will help theoretical probability researchers, applied statisticians, biologists, statistical physicists, and computer scientists work better across their own disciplinary boundaries.
Автор: Chjan Lim; Joseph Nebus Название: Vorticity, Statistical Mechanics, and Monte Carlo Simulation ISBN: 1441922474 ISBN-13(EAN): 9781441922472 Издательство: Springer Рейтинг: Цена: 20263.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is drawn from across many active fields of mathematics and physics. With fresh insights into an important field, the book addresses how to access interesting, original, and publishable research in statistical modeling of large-scale flows and related fields.
Автор: Nick T. Thomopoulos Название: Essentials of Monte Carlo Simulation ISBN: 1461460212 ISBN-13(EAN): 9781461460213 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. It illustrates the best ways to select input distributions and parameters with or without sample data.
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