Explorations In Monte Carlo Methods
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Author |
: Ronald W. Shonkwiler |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 249 |
Release |
: 2009-08-21 |
ISBN-10 |
: 9780387878362 |
ISBN-13 |
: 038787836X |
Rating |
: 4/5 (62 Downloads) |
Synopsis Explorations in Monte Carlo Methods by : Ronald W. Shonkwiler
Monte Carlo methods are among the most used and useful computational tools available today, providing efficient and practical algorithims to solve a wide range of scientific and engineering problems. Applications covered in this book include optimization, finance, statistical mechanics, birth and death processes, and gambling systems. Explorations in Monte Carlo Methods provides a hands-on approach to learning this subject. Each new idea is carefully motivated by a realistic problem, thus leading from questions to theory via examples and numerical simulations. Programming exercises are integrated throughout the text as the primary vehicle for learning the material. Each chapter ends with a large collection of problems illustrating and directing the material. This book is suitable as a textbook for students of engineering and the sciences, as well as mathematics.
Author |
: Thomas M. Carsey |
Publisher |
: SAGE Publications |
Total Pages |
: 304 |
Release |
: 2013-08-05 |
ISBN-10 |
: 9781483324920 |
ISBN-13 |
: 1483324923 |
Rating |
: 4/5 (20 Downloads) |
Synopsis Monte Carlo Simulation and Resampling Methods for Social Science by : Thomas M. Carsey
Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.
Author |
: Christian Robert |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 297 |
Release |
: 2010 |
ISBN-10 |
: 9781441915757 |
ISBN-13 |
: 1441915753 |
Rating |
: 4/5 (57 Downloads) |
Synopsis Introducing Monte Carlo Methods with R by : Christian Robert
This book covers the main tools used in statistical simulation from a programmer’s point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison.
Author |
: William L. Dunn |
Publisher |
: Elsevier |
Total Pages |
: 594 |
Release |
: 2022-06-07 |
ISBN-10 |
: 9780128197455 |
ISBN-13 |
: 0128197455 |
Rating |
: 4/5 (55 Downloads) |
Synopsis Exploring Monte Carlo Methods by : William L. Dunn
Exploring Monte Carlo Methods, Second Edition provides a valuable introduction to the numerical methods that have come to be known as "Monte Carlo." This unique and trusted resource for course use, as well as researcher reference, offers accessible coverage, clear explanations and helpful examples throughout. Building from the basics, the text also includes applications in a variety of fields, such as physics, nuclear engineering, finance and investment, medical modeling and prediction, archaeology, geology and transportation planning. - Provides a comprehensive yet concise treatment of Monte Carlo methods - Uses the famous "Buffon's needle problem" as a unifying theme to illustrate the many aspects of Monte Carlo methods - Includes numerous exercises and useful appendices on: Certain mathematical functions, Bose Einstein functions, Fermi Dirac functions and Watson functions
Author |
: James E. Gentle |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 252 |
Release |
: 2013-03-14 |
ISBN-10 |
: 9781475729603 |
ISBN-13 |
: 147572960X |
Rating |
: 4/5 (03 Downloads) |
Synopsis Random Number Generation and Monte Carlo Methods by : James E. Gentle
Monte Carlo simulation has become one of the most important tools in all fields of science. This book surveys the basic techniques and principles of the subject, as well as general techniques useful in more complicated models and in novel settings. The emphasis throughout is on practical methods that work well in current computing environments.
Author |
: Christian Robert |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 670 |
Release |
: 2013-03-14 |
ISBN-10 |
: 9781475741452 |
ISBN-13 |
: 1475741456 |
Rating |
: 4/5 (52 Downloads) |
Synopsis Monte Carlo Statistical Methods by : Christian Robert
We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
Author |
: Arnaud Doucet |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 590 |
Release |
: 2013-03-09 |
ISBN-10 |
: 9781475734379 |
ISBN-13 |
: 1475734379 |
Rating |
: 4/5 (79 Downloads) |
Synopsis Sequential Monte Carlo Methods in Practice by : Arnaud Doucet
Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.
Author |
: Enrico Zio |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 204 |
Release |
: 2012-11-02 |
ISBN-10 |
: 9781447145882 |
ISBN-13 |
: 1447145887 |
Rating |
: 4/5 (82 Downloads) |
Synopsis The Monte Carlo Simulation Method for System Reliability and Risk Analysis by : Enrico Zio
Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling. Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques. This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergraduate and graduate students as well as researchers and practitioners. It provides a powerful tool for all those involved in system analysis for reliability, maintenance and risk evaluations.
Author |
: Jun S. Liu |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 368 |
Release |
: 2008-01-04 |
ISBN-10 |
: 0387763694 |
ISBN-13 |
: 9780387763699 |
Rating |
: 4/5 (94 Downloads) |
Synopsis Monte Carlo Strategies in Scientific Computing by : Jun S. Liu
This book provides an up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. It can be used as a textbook for a graduate-level course on Monte Carlo methods.
Author |
: Dirk P. Kroese |
Publisher |
: John Wiley & Sons |
Total Pages |
: 627 |
Release |
: 2013-06-06 |
ISBN-10 |
: 9781118014950 |
ISBN-13 |
: 1118014952 |
Rating |
: 4/5 (50 Downloads) |
Synopsis Handbook of Monte Carlo Methods by : Dirk P. Kroese
A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field. The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including: Random variable and stochastic process generation Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run Discrete-event simulation Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo Estimation of derivatives and sensitivity analysis Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation. Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.