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EBookClubs

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Book Bayesian Smoothing and Step Functions in the Nonparametric Estimation of Curves and Surfaces

Download or read book Bayesian Smoothing and Step Functions in the Nonparametric Estimation of Curves and Surfaces written by Juha Heikkinen and published by . This book was released on 1997 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engl. und finn. Zusammenfassung.

Book Proceedings of the Section on Bayesian Statistical Science

Download or read book Proceedings of the Section on Bayesian Statistical Science written by American Statistical Association. Section on Bayesian Statistical Science and published by . This book was released on 1998 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Practical Nonparametric and Semiparametric Bayesian Statistics

Download or read book Practical Nonparametric and Semiparametric Bayesian Statistics written by Dipak D. Dey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.

Book AMSTAT News

    Book Details:
  • Author : American Statistical Association
  • Publisher :
  • Release : 2003
  • ISBN :
  • Pages : 436 pages

Download or read book AMSTAT News written by American Statistical Association and published by . This book was released on 2003 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Highly Structured Stochastic Systems

Download or read book Highly Structured Stochastic Systems written by Peter J. Green and published by . This book was released on 2003 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through this text, the author aims to make recent developments in the title subject (a modern strategy for the creation of statistical models to solve 'real world' problems) accessible to graduate students and researchers in the field of statistics.

Book Mathematical Reviews

Download or read book Mathematical Reviews written by and published by . This book was released on 2003 with total page 812 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Bayesian Data Analysis  Third Edition

Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Journal of the American Statistical Association

Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2008 with total page 920 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Bayesian inference with INLA

Download or read book Bayesian inference with INLA written by Virgilio Gomez-Rubio and published by CRC Press. This book was released on 2020-02-20 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.

Book Scientific and Technical Aerospace Reports

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1992 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Image Processing and Jump Regression Analysis

Download or read book Image Processing and Jump Regression Analysis written by Peihua Qiu and published by John Wiley & Sons. This book was released on 2005-05-20 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first text to bridge the gap between image processing andjump regression analysis Recent statistical tools developed to estimate jump curves andsurfaces have broad applications, specifically in the area of imageprocessing. Often, significant differences in technicalterminologies make communication between the disciplines of imageprocessing and jump regression analysis difficult. Ineasy-to-understand language, Image Processing and JumpRegression Analysis builds a bridge between the worlds ofcomputer graphics and statistics by addressing both the connectionsand the differences between these two disciplines. The authorprovides a systematic analysis of the methodology behindnonparametric jump regression analysis by outlining procedures thatare easy to use, simple to compute, and have proven statisticaltheory behind them. Key topics include: Conventional smoothing procedures Estimation of jump regression curves Estimation of jump location curves of regression surfaces Jump-preserving surface reconstruction based on localsmoothing Edge detection in image processing Edge-preserving image restoration With mathematical proofs kept to a minimum, this book isuniquely accessible to a broad readership. It may be used as aprimary text in nonparametric regression analysis and imageprocessing as well as a reference guide for academicians andindustry professionals focused on image processing or curve/surfaceestimation.

Book All of Statistics

    Book Details:
  • Author : Larry Wasserman
  • Publisher : Springer Science & Business Media
  • Release : 2013-12-11
  • ISBN : 0387217363
  • Pages : 446 pages

Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Book Bayesian Nonparametrics

    Book Details:
  • Author : J.K. Ghosh
  • Publisher : Springer Science & Business Media
  • Release : 2006-05-11
  • ISBN : 0387226540
  • Pages : 311 pages

Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.