Monte Carlo Methods in Bayesian Computation. M.-H. Chen, Q.-M. Shao, J.G. Ibrahim.,
Автор: Glasserman Название: Monte Carlo Methods in Financial Engineering ISBN: 0387004513 ISBN-13(EAN): 9780387004518 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: From the reviews: "Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers [...] So often, financial engineering texts are very theoretical. This book is not."
Автор: Shao Jun Название: Mathematical Statistics ISBN: 0387953825 ISBN-13(EAN): 9780387953823 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are useful in statistics.
Автор: Victor H. Pe?a; Tze Leung Lai; Qi-Man Shao Название: Self-Normalized Processes ISBN: 3642099262 ISBN-13(EAN): 9783642099267 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume covers recent developments in self-normalized processes, including self-normalized large and moderate deviations, and laws of the iterated logarithms for self-normalized martingales.
Автор: Shao Название: Mathematical Statistics: Exercises and Solutions ISBN: 0387249702 ISBN-13(EAN): 9780387249704 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The exercises are grouped into seven chapters with titles matching those in the author`s Mathematical Statistics. Can also be used as a stand-alone because exercises and solutions are comprehensible independently of their source, and notation and terminology are explained in the front of the book.
Автор: Jun Shao; Dongsheng Tu Название: The Jackknife and Bootstrap ISBN: 1461269032 ISBN-13(EAN): 9781461269038 Издательство: Springer Рейтинг: Цена: 46118.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The resampling methods replace theoreti- cal derivations required in applying traditional methods (such as substitu- tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples.
Описание: This book provides state-of-the-art and interdisciplinary topics on solving matrix eigenvalue problems, particularly by using recent petascale and upcoming post-petascale supercomputers.
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.
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: Wakefield Название: Bayesian and Frequentist Regression Methods ISBN: 1441909249 ISBN-13(EAN): 9781441909244 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis.
Автор: Ronald W. Shonkwiler; Franklin Mendivil Название: Explorations in Monte Carlo Methods ISBN: 1489983791 ISBN-13(EAN): 9781489983794 Издательство: Springer Рейтинг: Цена: 6981.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Monte Carlo methods are among the most used and useful computational tools available, providing efficient and practical algorithms to solve a wide range of scientific and engineering problems. This book provides a hands-on approach to learning this subject.
Автор: Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G. Название: Monte Carlo Methods in Bayesian Computation ISBN: 0387989358 ISBN-13(EAN): 9780387989358 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches.The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.
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