Bayesian Economics Through Numerical Methods, Jeffrey H. Dorfman

Автор: Sandmo, Agnar
Название: Economics evolving: A History of Economic Thought
ISBN: 0691148422 ISBN-13(EAN): 9780691148427
Издательство: Wiley
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Описание: Describes the history of economic thought, focusing on the development of economic theory from Adam Smith`s "Wealth of Nations" to the late twentieth century. This text examines how important economists have reflected on the sometimes conflicting goals of efficient resource use and socially acceptable income distribution.

Автор: LeVeque Randall J.
Название: Numerical Methods for Conservation Laws
ISBN: 3764327235 ISBN-13(EAN): 9783764327231
Издательство: Springer
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Описание: These notes were developed for a graduate-level course on the theory and numerical solution of nonlinear hyperbolic systems of conservation laws. Part I deals with the basic mathematical theory of the equations: the notion of weak solutions, entropy conditions, and a detailed description of the wave structure of solutions to the Riemann problem. The emphasis is on tools and techniques that are indispensable in developing good numerical methods for discontinuous solutions. Part II is devoted to the development of high resolution shock-capturing methods, including the theory of total variation diminishing (TVD) methods and the use of limiter functions. The book is intended for a wide audience, and will be of use both to numerical analysts and to computational researchers in a variety of applications.

Автор: Monahan
Название: Numerical Methods of Statistics
ISBN: 0521191580 ISBN-13(EAN): 9780521191586
Издательство: Cambridge Academ
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Описание: This second edition explains how computer software is designed to perform the tasks required for sophisticated statistical analysis.

Автор: Todor Boyanov; Stefka Dimova; Krassimir Georgiev;
Название: Numerical Methods and Applications
ISBN: 3540709401 ISBN-13(EAN): 9783540709404
Издательство: Springer
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Описание: Constitutes the thoroughly refereed post-proceedings of NMA 2006 held in Borovets, Bulgaria. This book includes numerical methods for hyperbolic problems, robust preconditioning solution methods, metaheuristics for optimization problems, uncertain/control systems and reliable numerics, and interpolation and quadrature processes.

Автор: Joseph J.K. O Ruanaidh; William J. Fitzgerald
Название: Numerical Bayesian Methods Applied to Signal Processing
ISBN: 146126880X ISBN-13(EAN): 9781461268802
Издательство: Springer
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Цена: 27950.00 р.
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Описание: The fundamental theory behind Digital Signal Process- ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous- tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87].

Автор: Wakefield
Название: Bayesian and Frequentist Regression Methods
ISBN: 1441909249 ISBN-13(EAN): 9781441909244
Издательство: Springer
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Описание: Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis.

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
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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: Xiu, Dongbin
Название: Numerical methods for stochastic computations
ISBN: 0691142122 ISBN-13(EAN): 9780691142128
Издательство: Wiley
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Описание: Focusing on fundamental aspects of numerical methods for stochastic computations, this book describes the class of numerical methods based on generalized polynomial chaos (gPC). It illustrates through examples Basic gPC methods, and includes polynomial approximation theory and probability theory.