bayesian nonparametrics springer series in statistics

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Bayesian Nonparametrics

Author : J.K. Ghosh
ISBN : 9780387226545
Genre : Mathematics
File Size : 71. 8 MB
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This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.

Bayesian Nonparametric Data Analysis

Author : Peter Müller
ISBN : 9783319189680
Genre : Mathematics
File Size : 84. 68 MB
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This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.

Asymptotics Nonparametrics And Time Series

Author : Subir Ghosh
ISBN : 0824700511
Genre : Mathematics
File Size : 56. 84 MB
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"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."

Nonparametric Statistics

Author : Deshpande Jayant V
ISBN : 9789814663595
Genre : Mathematics
File Size : 48. 29 MB
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The number of books on Nonparametric Methodology is quite small as compared to, say, on Design of Experiments, Regression Analysis, Multivariate Analysis, etc. Because of being perceived as less effective, nonparametric methods are still the second choice. Actually, it has been demonstrated time and again that they are useful. We feel that there is still need for proper texts/applications/reference books on Nonparametric Methodology. This book will introduce various types of data encountered in practice and suggest the appropriate nonparametric methods, discuss their properties through null and non-null distributions whenever possible and demonstrate the very minor loss in power and efficiency in the nonparametric method, if any. The book will cover almost all topics of current interest such as bootstrapping, ranked set sampling, techniques for censored data and Bayesian analysis under nonparametric set ups.

Statistical Tools For Nonlinear Regression

Author : Sylvie Huet
ISBN : 9780387215747
Genre : Mathematics
File Size : 29. 12 MB
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Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.

Bayesian Nonparametric Analysis Of Conditional Distributions And Inference For Poisson Point Processes

Author : Matthew Alan Taddy
ISBN : UCAL:X79090
Genre :
File Size : 74. 70 MB
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Prior Processes And Their Applications

Author : Eswar G. Phadia
ISBN : 9783319327891
Genre : Mathematics
File Size : 79. 95 MB
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This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form. However, the current interest in modeling and treating large-scale and complex data also poses a problem – the posterior distribution, which is essential to Bayesian analysis, is invariably not in a closed form, making it necessary to resort to simulation. Accordingly, the book also introduces several computational procedures, such as the Gibbs sampler, Blocked Gibbs sampler and slice sampling, highlighting essential steps of algorithms while discussing specific models. In addition, it features crucial steps of proofs and derivations, explains the relationships between different processes and provides further clarifications to promote a deeper understanding. Lastly, it includes a comprehensive list of references, equipping readers to explore further on their own.

Bayesian Statistics And Its Applications

Author : Satyanshu K. Upadhyay
ISBN : 190574000X
Genre : Mathematics
File Size : 75. 84 MB
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In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.

Bayesian Nonparametrics For Inference Of Ecological Dynamics

Author : Anand Patil
ISBN : UCAL:X77672
Genre :
File Size : 69. 94 MB
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Handbook Of Income Distribution

Author : Anthony B. Atkinson
ISBN : 9780444594761
Genre : Social Science
File Size : 30. 61 MB
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What new theories, evidence, explanations, and policies have shaped our studies of income distribution in the 21st century? Editors Tony Atkinson and Francois Bourguignon assemble the expertise of leading authorities in this survey of substantive issues. In two volumes they address subjects that were not covered in Volume 1 (2000), such as education, health and experimental economics; and subjects that were covered but where there have been substantial new developments, such as the historical study of income inequality and globalization. Some chapters discuss future growth areas, such as inheritance, the links between inequality and macro-economics and finance, and the distributional implications of climate change. They also update empirical advances and major changes in the policy environment. The volumes define and organize key areas of income distribution studies Contributors focus on identifying newly developing questions and opportunities for future research The authoritative articles emphasize the ways that income mobility and inequality studies have recently gained greater political significance

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