Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 73920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Автор: Ghosh J.K., Ramamoorthi R.V. Название: Bayesian Nonparametrics ISBN: 0387955372 ISBN-13(EAN): 9780387955377 Издательство: Springer Рейтинг: Цена: 153720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter onJayanta Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently professor of statistics at Purdue University. He has been editor of Sankhya and served on the editorial boards of several journals including the Annals of Statistics. Apart from Bayesian analysis, his interests include asymptotics, stochastic modeling, high dimensional model selection, reliability and survival analysis and bioinformatics.R.V. Ramamoorthi is professor at the Department of Statistics and Probability at Michigan State University. He has published papers in the areas of sufficiency invariance, comparison of experiments, nonparametric survival analysis and Bayesian analysis. In addition to Bayesian nonparametrics, he is currently interested in Bayesian networks and graphical models. He is on the editorial board of Sankhya.
Автор: Wakefield Название: Bayesian and Frequentist Regression Methods ISBN: 1441909249 ISBN-13(EAN): 9781441909244 Издательство: Springer Рейтинг: Цена: 102480.00 T 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: Thompson John Название: Bayesian Analysis with Stata ISBN: 1597181412 ISBN-13(EAN): 9781597181419 Издательство: Taylor&Francis Рейтинг: Цена: 57150.00 T Наличие на складе: Невозможна поставка. Описание:
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: Gelman Название: Bayesian Data Analysis, Third Edition ISBN: 1439840954 ISBN-13(EAN): 9781439840955 Издательство: Taylor&Francis Рейтинг: Цена: 73920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
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: 139750.00 T 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.
Автор: Ntzoufras, Ioannis Название: Bayesian Modeling Using WinBUGS ISBN: 047014114X ISBN-13(EAN): 9780470141144 Издательство: Wiley Рейтинг: Цена: 147790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Detailed examples will be provided ranging from the very basic to the more advanced; they will also reflect realistic data sets (available from the Internet). An underlying emphasis is given to Generalized Linear Models (GLMs) that are familiar to most readers and researchers.
Автор: Greenberg Название: Introduction to Bayesian Econometrics ISBN: 1107015316 ISBN-13(EAN): 9781107015319 Издательство: Cambridge Academ Рейтинг: Цена: 53850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This textbook is an introduction to econometrics from the Bayesian viewpoint. New material includes a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The R programming language is also emphasized.
Автор: Jon Wakefield Название: Bayesian and Frequentist Regression Methods ISBN: 1493938622 ISBN-13(EAN): 9781493938629 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a balanced, modern introduction to Bayesian and frequentist methods for regression analysis. The author discusses Frequentist and Bayesian Inferences; Linear Models; Binary Data Models; General Regression Models and Survival Models.
Автор: Muller, P., Quintana, F.A., Jara, A., Hanson, T. Название: Bayesian Nonparametric Data Analysis ISBN: 3319189670 ISBN-13(EAN): 9783319189673 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.
Автор: Francisco J. Samaniego Название: A Comparison of the Bayesian and Frequentist Approaches to Estimation ISBN: 1461426197 ISBN-13(EAN): 9781461426196 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Suitable for an audience having a solid grounding in probability and statistics at the level of the year-long undergraduate course taken by statistics and mathematics majors, this book presents the necessary background on Decision Theory and the frequentist and Bayesian approaches to estimation.
Автор: Mary Kathryn Cowles Название: Applied Bayesian Statistics ISBN: 1489997040 ISBN-13(EAN): 9781489997043 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Based on the author`s extensive experience in both statistics and education, this book imparts the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. Demonstrates applications to real-world data.
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