Mean Field Simulation for Monte Carlo Integration, Del Moral, Pierre

Автор: Del Moral
Название: Mean Field Simulation for Monte Carlo Integration
ISBN: 1138198730 ISBN-13(EAN): 9781138198739
Издательство: Taylor&Francis
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Цена: 53070.00 T
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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.

Название: Stochastic Simulation and Monte Carlo Methods
ISBN: 3642393624 ISBN-13(EAN): 9783642393624
Издательство: Springer
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Цена: 55890.00 T
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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: Gamerman, Dani.
Название: Markov Chain Monte Carlo
ISBN: 1584885874 ISBN-13(EAN): 9781584885870
Издательство: Taylor&Francis
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Цена: 102080.00 T
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Описание: Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection.

Автор: Peter Kritzer, Harald Niederreiter, Friedrich Pill
Название: Uniform Distribution and Quasi-Monte Carlo Methods: Discrepancy, Integration and Applications
ISBN: 3110317893 ISBN-13(EAN): 9783110317893
Издательство: Walter de Gruyter
Цена: 173490.00 T
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Описание: This book is summarizing the results of the workshop "Uniform Distribution and Quasi-Monte Carlo Methods" of the RICAM Special Semester on "Applications of Algebra and Number Theory" in October 2013. The survey articles in this book focus on number theoretic point constructions, uniform distribution theory, and quasi-Monte Carlo methods. As deterministic versions of the Monte Carlo method, quasi-Monte Carlo rules enjoy increasing popularity, with many fruitful applications in mathematical practice, as for example in finance, computer graphics, and biology. The goal of this book is to give an overview of recent developments in uniform distribution theory, quasi-Monte Carlo methods, and their applications, presented by leading experts in these vivid fields of research.

Автор: L. Bauwens
Название: Bayesian Full Information Analysis of Simultaneous Equation Models Using Integration by Monte Carlo
ISBN: 3540133844 ISBN-13(EAN): 9783540133841
Издательство: Springer
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Цена: 102480.00 T
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Описание: In their review of the "Bayesian analysis of simultaneous equation systems", Dr ze and Richard (1983) - hereafter DR - express the following viewpoint about the present state of development of the Bayesian full information analysis of such sys- tems i) the method allows "a flexible specification of the prior density, including well defined noninformative prior measures"; ii) it yields "exact finite sample posterior and predictive densities". However, they call for further developments so that these densities can be eval- uated through 'numerical methods, using an integrated software packa e. To that end, they recommend the use of a Monte Carlo technique, since van Dijk and Kloek (1980) have demonstrated that "the integrations can be done and how they are done". In this monograph, we explain how we contribute to achieve the developments suggested by Dr ze and Richard. A basic idea is to use known properties of the porterior density of the param- eters of the structural form to design the importance functions, i. e. approximations of the posterior density, that are needed for organizing the integrations.

Автор: Nick T. Thomopoulos
Название: Essentials of Monte Carlo Simulation
ISBN: 1489986081 ISBN-13(EAN): 9781489986085
Издательство: Springer
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Цена: 102480.00 T
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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: Enrico Zio
Название: The Monte Carlo Simulation Method for System Reliability and Risk Analysis
ISBN: 1447159012 ISBN-13(EAN): 9781447159018
Издательство: Springer
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Цена: 130590.00 T
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Описание: This book illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Conveys a sound understanding of of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling.

Автор: Rubinstein Reuven Y.
Название: Simulation and the Monte Carlo Method
ISBN: 1118632168 ISBN-13(EAN): 9781118632161
Издательство: Wiley
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Цена: 116110.00 T
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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
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Цена: 191560.00 T
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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.

Автор: Chjan Lim; Joseph Nebus
Название: Vorticity, Statistical Mechanics, and Monte Carlo Simulation
ISBN: 1441922474 ISBN-13(EAN): 9781441922472
Издательство: Springer
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Цена: 135090.00 T
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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: Reuven Y. Rubinstein; Dirk P. Kroese
Название: The Cross-Entropy Method
ISBN: 1441919406 ISBN-13(EAN): 9781441919403
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book is a comprehensive and accessible introduction to the cross-entropy (CE) method. The CE method started life around 1997 when the first author proposed an adaptive algorithm for rare-event simulation using a cross-entropy minimization technique. It was soon realized that the underlying ideas had a much wider range of application than just in rare-event simulation; they could be readily adapted to tackle quite general combinatorial and multi-extremal optimization problems, including many problems associated with the field of learning algorithms and neural computation. The book is based on an advanced undergraduate course on the CE method, given at the Israel Institute of Technology (Technion) for the last three years. It is aimed at a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist or practitioner, who is interested in smart simulation, fast optimization, learning algorithms, image processing, etc. Our aim was to write a book on the CE method which was accessible to advanced undergraduate students and engineers who simply want to apply the CE method in their work, while at the same time accentu- ating the unifying and novel mathematical ideas behind the CE method, so as to stimulate further research at a postgraduate level.

Автор: Nick T. Thomopoulos
Название: Essentials of Monte Carlo Simulation
ISBN: 1461460212 ISBN-13(EAN): 9781461460213
Издательство: Springer
Рейтинг:
Цена: 139750.00 T
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.