7 edition of **Monte Carlo Methods (CRC Monographs on Statistics & Applied Probability)** found in the catalog.

Monte Carlo Methods (CRC Monographs on Statistics & Applied Probability)

J. Hammersley

- 362 Want to read
- 40 Currently reading

Published
**June 25, 1979**
by Chapman & Hall
.

Written in English

- Probability & statistics,
- General,
- Science / General,
- Science

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 178 |

ID Numbers | |

Open Library | OL9317069M |

ISBN 10 | 0412158701 |

ISBN 10 | 9780412158704 |

Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method. A very authoritative source is the book by Robert and Casella - Monte Carlo Statistical Methods - In addition, I very strongly recommend videos and papers by Nando deFreitas. It’s not quite introductory (which is a subjective assessment anyway) but they are very nicely prepared.

Monte Carlo methods. S Green, Z Ghani and F Fiorini Books links. Book table of contents. About ePub3. About IOP ebooks. The Monte Carlo technique offers the possibility of performing a complete simulation of a radiation shielding facility in all its geometrical complexity. In principle, the outcomes from such simulations are limited in. MONTE CARLO METHODS. whic h in the discrete case is just rewriting equation When. x. is discrete, the exp ectation corresp onds to a sum, and when. x. is contin uous, the exp ectation. corresp onds to an in tegral. Regardless of whether the state is con tin uous or discrete, all Marko v chain.

The book is well organized; the flow of topics follows a logical development. The coverage is up-to-date and comprehensive, and so the book is a good resource for people conducting research on Monte Carlo methods. The book would be an excellent supplementary text for a course in scientific computing ." (SIAM Review)Brand: Springer-Verlag New York. An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios/5(16).

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He teaches and conducts research in radiation transport, radiation shielding, Monte Carlo methods, reactor physics, optimization of new type of radiation detectors, numerical analysis, particle combustion, remote sensing, and utility energy and economic by: "Professor Haghighat, based on his many years of experience in teaching the subject, has written a long-awaited book on Monte Carlo methods.

The subject of the book based on particle transport has an old history in concept, but is becoming lately more important and enjoying heavy use with the advent of high-performance by: This introduction to Monte Carlo Methods seeks to identify and study the unifying elements that underlie their effective application.

The book focuses on two basic themes. The first is the importance of random walks as they occur both in natural stochastic systems and Cited by: The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms.

All chapters include exercises and all R programs are available as an R package called by: The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.

The Monte Carlo method or method of statistical trials consists of solving various problems of computational mathematics by means of the construction of some random process for each such problem, with the parameters of the process equal to the required quantities of the problem.

Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics Book 10) by Reuven Y. Rubinstein and Dirk P. Kroese out of 5 stars 2.

Based on the author's own experience, Monte Carlo Methods in Finance adopts a practical flavour throughout, the emphasis being on financial modelling and derivatives pricing.

Numerous real world examples help the reader foster an intuitive grasp of the mathematical and numerical techniques needed to solve particular financial by: Monte Carlo statistical methods, particularly those based on Markov chains, are now an essential component of the standard set of techniques used by statisticians.

This new edition has been revised towards a coherent and flowing coverage of these simulation techniques, with incorporation of the most recent developments in the field. Monte Carlo theory, methods and examples I have a book in progress on Monte Carlo, quasi-Monte Carlo and Markov chain Monte Carlo.

Several of the chapters are polished enough to place here. I'm interested in comments especially about errors or suggestions for references to include. Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo.

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.

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He has published over articles and four books in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic c theory, reliability, computational statistics, applied probability, and stochastic by: 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. Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques.

This book develops the use of Monte Carlo methods inBrand: Springer-Verlag New York. Monte Carlo (MC) methods are a subset of computational algorithms that use the process of repeated random sampling to make numerical estimations of unknown parameters.

They allow for the modeling of complex situations where many random variables Author: Christopher Pease. Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation theories presented in this text deal with systems that are too complex to solve analytically.

As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. In this book you find anything you need for Monte Carlo (and Quasi Monte Carlo) methods. As the title says, the applications given are all from finance, but nevertheless it is an excellent book to give you an understanding of the different methods especially for variance reduction/5.

Monte Carlo Methods book. Read reviews from world’s largest community for readers. This introduction to Monte Carlo methods seeks to identify and study t /5(2). The Monte Carlo Method: The Method of Statistical Trials is a systematic account of the fundamental concepts and techniques of the Monte Carlo method, together with its range of applications.

Some of these applications include the computation of definite integrals, neutron physics, and in the investigation of servicing Edition: 1. This book provides an introduction to Monte Carlo simulations in classical statistical physics and is aimed both at students beginning work in the field and at more experienced researchers who wish to learn more about Monte Carlo methods.4/5(9).The text then applies the Exodus method to Laplace’s and Poisson’s equations and presents Monte Carlo techniques for handing Neumann problems.

It also deals with whole field computation using the Markov chain, applies Monte Carlo methods to time-varying diffusion problems, and explores wave scattering due to random rough surfaces.