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Robust Bayesian Analysis And Optimal Experimental Designs In Normal Linear Models With Many Parameters
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Book Synopsis Robust Bayesian Analysis and Optimal Experimental Designs in Normal Linear Models with Many Parameters by : A. DasGupta
Download or read book Robust Bayesian Analysis and Optimal Experimental Designs in Normal Linear Models with Many Parameters written by A. DasGupta and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Estimation and Experimental Design in Linear Regression Models by : Jürgen Pilz
Download or read book Bayesian Estimation and Experimental Design in Linear Regression Models written by Jürgen Pilz and published by . This book was released on 1991-07-09 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.
Book Synopsis Bayesian Optimal Experimental Design by : Ine Steyls
Download or read book Bayesian Optimal Experimental Design written by Ine Steyls and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Analysis of Linear Models by : Broemeling
Download or read book Bayesian Analysis of Linear Models written by Broemeling and published by CRC Press. This book was released on 2017-11-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus, this definitive volume containssomething traditional-a review of Bayesian techniques and methods of estimation, hypothesis,testing, and forecasting as applied to the standard populations ... somethinginnovative-a new approach to mixed models and models not generally studied by statisticianssuch as linear dynamic systems and changing parameter models ... and somethingpractical-clear graphs, eary-to-understand examples, end-of-chapter problems, numerousreferences, and a distribution appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text isideal for students in graduate-level courses such as linear models, econometrics, andBayesian inference.
Book Synopsis Optimal Bayesian Experimental Design in the Presence of Model Error by :
Download or read book Optimal Bayesian Experimental Design in the Presence of Model Error written by and published by . This book was released on 2015 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction. We propose an information theoretic framework and algorithms for robust optimal experimental design with simulation-based models, with the goal of maximizing information gain in targeted subsets of model parameters, particularly in situations where experiments are costly. Our framework employs a Bayesian statistical setting, which naturally incorporates heterogeneous sources of information. An objective function reflects expected information gain from proposed experimental designs. Monte Carlo sampling is used to evaluate the expected information gain, and stochastic approximation algorithms make optimization feasible for computationally intensive and high-dimensional problems. A key aspect of our framework is the introduction of model calibration discrepancy terms that are used to "relax" the model so that proposed optimal experiments are more robust to model error or inadequacy. We illustrate the approach via several model problems and misspecification scenarios. In particular, we show how optimal designs are modified by allowing for model error, and we evaluate the performance of various designs by simulating "real-world" data from models not considered explicitly in the optimization objective.
Book Synopsis Bayesian Calibration Experimental Designs Based on Linear Models by : Sung Chul Kim
Download or read book Bayesian Calibration Experimental Designs Based on Linear Models written by Sung Chul Kim and published by . This book was released on 1988 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Bayesian Optimal Designs by : Han Son Seo
Download or read book Robust Bayesian Optimal Designs written by Han Son Seo and published by . This book was released on 1992 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Roussos Dimitrakopoulos Publisher :Springer Science & Business Media ISBN 13 :9401108242 Total Pages :513 pages Book Rating :4.4/5 (11 download)
Book Synopsis Geostatistics for the Next Century by : Roussos Dimitrakopoulos
Download or read book Geostatistics for the Next Century written by Roussos Dimitrakopoulos and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: To honour the remarkable contribution of Michel David in the inception, establishment and development of Geostatistics, and to promote the essence of his work, an international Forum entitled Geostatistics for the Next Century was convened in Montreal in June 1993. In order to enhance communication and stimulate geostatistical innovation, research and development, the Forum brought together world leading researchers and practitioners from five continents, who discussed-debated current problems, new technologies and futuristic ideas. This volume contains selected peer-reviewed papers from the Forum, together with comments by participants and replies by authors. Although difficult to capture the spontaneity and range of a debate, comments and replies should further assist in the promotion of ideas, dialogue and criticism, and are consistent with the spirit of the Forum. The contents of this volume are organized following the Forum's thematic sessions. The role of theme sessions was not only to stress important topics of tOday but in addition, to emphasize common ground held among diverse areas of geostatistical work and the need to strengthen communication between these areas. For this reason, any given section of this book may include papers from theory to applications, in mining, petroleum, environment, geohydrology, image processing.
Book Synopsis Sequential Analysis and Optimal Design by : Herman Chernoff
Download or read book Sequential Analysis and Optimal Design written by Herman Chernoff and published by SIAM. This book was released on 1972-01-01 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: An exploration of the interrelated fields of design of experiments and sequential analysis with emphasis on the nature of theoretical statistics and how this relates to the philosophy and practice of statistics.
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
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 Synopsis Journal of Statistical Planning and Inference by :
Download or read book Journal of Statistical Planning and Inference written by and published by . This book was released on 1992 with total page 860 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Bayesian Analysis of a Parameter Change in Linear Regression by : Klaus Pötzelberger
Download or read book Robust Bayesian Analysis of a Parameter Change in Linear Regression written by Klaus Pötzelberger and published by . This book was released on 1988 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimal Bayesian Experimental Design for Linear Models (bayesian Optimal Design) by : Kathryn Chaloner
Download or read book Optimal Bayesian Experimental Design for Linear Models (bayesian Optimal Design) written by Kathryn Chaloner and published by . This book was released on 1983 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Frontiers of Statistical Decision Making and Bayesian Analysis by : Ming-Hui Chen
Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Book Synopsis Optimal Design of Experiments by : Friedrich Pukelsheim
Download or read book Optimal Design of Experiments written by Friedrich Pukelsheim and published by SIAM. This book was released on 2006-04-01 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimal Design of Experiments offers a rare blend of linear algebra, convex analysis, and statistics. The optimal design for statistical experiments is first formulated as a concave matrix optimization problem. Using tools from convex analysis, the problem is solved generally for a wide class of optimality criteria such as D-, A-, or E-optimality. The book then offers a complementary approach that calls for the study of the symmetry properties of the design problem, exploiting such notions as matrix majorization and the Kiefer matrix ordering. The results are illustrated with optimal designs for polynomial fit models, Bayes designs, balanced incomplete block designs, exchangeable designs on the cube, rotatable designs on the sphere, and many other examples.
Book Synopsis Generalized Linear Models by : Dipak K. Dey
Download or read book Generalized Linear Models written by Dipak K. Dey and published by CRC Press. This book was released on 2000-05-25 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers
Book Synopsis Bayesian Approaches to Model Robust and Model Discrimination Designs by : Vincent Kokouvi Agboto
Download or read book Bayesian Approaches to Model Robust and Model Discrimination Designs written by Vincent Kokouvi Agboto and published by . This book was released on 2006 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:
