Monte Carlo Methods in Bayesian Computation, Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G.
Автор: Thompson John Название: Bayesian Analysis with Stata ISBN: 1597181412 ISBN-13(EAN): 9781597181419 Издательство: Taylor&Francis Рейтинг: Цена: 6467 р. Наличие на складе: Поставка под заказ.
Bayesian Analysis with Stata is written for anyone interested in applying Bayesian methods to real data easily. The book shows how modern analyses based on Markov chain Monte Carlo (MCMC) methods are implemented in Stata both directly and by passing Stata datasets to OpenBUGS or WinBUGS for computation, allowing Stata's data management and graphing capability to be used with OpenBUGS/WinBUGS speed and reliability.
The book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational efficiency and speed, the assessment of convergence, the evaluation of models, and the presentation of the results. Every topic is illustrated in detail using real-life examples, mostly drawn from medical research.
The book takes great care in introducing concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The book's content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.
Автор: Dimov Ivan Название: Monte carlo methods for applied scientists ISBN: 9810223293 ISBN-13(EAN): 9789810223298 Издательство: World Scientific Publishing Рейтинг: Цена: 13805 р. Наличие на складе: Поставка под заказ.
Описание: The Monte Carlo method is inherently parallel and the extensive and rapid development in vector and parallel computers has resulted in renewed and
increasing interest in this method. At the same time there has been an expansion in the application areas and the method is now widely used in many important areas of science
including nuclear and semiconductor physics, statistical mechanics and heat and mass transfer. This work attempts to bridge the gap between theory and practice concentrating on
modern algorithmic implementation on parallel architecture machines.
Although a suitable text for final year or postgraduate mathematicians it is principally aimed at the applied
scientists - only a small amount of mathematical knowledge is assumed and theorem proving is kept to a minimum, with the main focus being on parallel algorithm development often to
applied industrial problems. Algorithms are developed both for MIMD machines with distributed memory and SIMD machines; a selection of programs are provided.
Автор: Albert, Jim Название: Bayesian computation with R ISBN: 0387922970 ISBN-13(EAN): 9780387922973 Издательство: Springer Рейтинг: Цена: 6269 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: There has been a dramatic growth in the development and application of Bayesian inferential methods. This book introduces Bayesian modeling by the use of computation using the R language. The new edition contains changes in the R code illustrations.
Описание: Statistical methodology plays a key role in ensuring that DNA evidence is collected, interpreted, analyzed, and presented correctly. With the recent advances in computer technology, this methodology is more complex than ever before. There are a growing number of books in the area but none are devoted to the computational analysis of evidence.
Автор: Kalos Название: Monte Carlo Methods, 2nd Edition ISBN: 352740760X ISBN-13(EAN): 9783527407606 Издательство: Wiley Рейтинг: Цена: 15015 р. Наличие на складе: Поставка под заказ.
Описание: This is a revised and extended edition of 'Monte Carlo Methods', first published by Wiley in 1986 and still in print (life sales as of 15 Jan. 2007: 3,158 copies). There have been enormous advances in Monte Carlo methods and their applications in the meantime, and the authors propose to bring the treatment up to date while retaining the elementary but general approach.
Описание: This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level. Written by authorities in the field, it places great emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration.
Описание: Offers a self-contained introduction to the theory and application of Bayesian statistical methods. This book begins with fundamental notions such as probability, exchangeability and Bayes` rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation.
Автор: Glasserman Название: Monte Carlo Methods in Financial Engineering ISBN: 0387004513 ISBN-13(EAN): 9780387004518 Издательство: Springer Рейтинг: Цена: 7314 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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 in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios.The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential.The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry.Mathematical Reviews, 2004: "... this book is very comprehensive, up-to-date and useful tool for those who are interested in implementing Monte Carlo methods in a financial context."
Описание: This second edition of G. Winkler's successful book on random field approaches to image analysis, related Markov Chain Monte Carlo methods, and statistical inference with emphasis on Bayesian image analysis concentrates more on general principles and models and less on details of concrete applications. Addressed to students and scientists from mathematics, statistics, physics, engineering, and computer science, it will serve as an introduction to the mathematical aspects rather than a survey. Basically no prior knowledge of mathematics or statistics is required.The second edition is in many parts completely rewritten and improved, and most figures are new. The topics of exact sampling and global optimization of likelihood functions have been added. This second edition comes with a CD-ROM by F. Friedrich,containing a host of (live) illustrations for each chapter. In an interactive environment, readers can perform their own experiments to consolidate the subject.
Автор: Robert Название: Monte Carlo Statistical Methods ISBN: 0387212396 ISBN-13(EAN): 9780387212395 Издательство: Springer Рейтинг: Цена: 11494 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
Описание: Surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. This book covers basic principles, as well as various methods such as parallel random number generation, nonlinear congruential generators, quasi Monte Carlo methods, and Markov chain Monte Carlo.
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