Bayesian Statistical Modelling (Wiley Series in Probability and Statistics)

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9780470018750: Bayesian Statistical Modelling (Wiley Series in Probability and Statistics)
Review:

"This text is ideal for researchers in applied statistics, medical sciences, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students." (Zentralblatt MATH, 2010)

From the Publisher:

Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition:* Provides an integrated presentation of theory, examples, applications and computer algorithms.* Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.* Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.* Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.* Provides exercises designed to help reinforce the reader's knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: "It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains." - ISI - Short Book Reviews "This is an excellent introductory book on Bayesian modelling techniques and data analysis" - Biometrics "The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods." - Journal of Mathematical Psychology

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ISBN 10: 0470018755 ISBN 13: 9780470018750
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Descripción Wiley. Hardcover. Estado de conservación: New. 0470018755 Brand new, hardback, 2nd edition, Bayesian Statistical Modelling, Wiley Publishing. Nº de ref. de la librería SKU1017676

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Descripción Hardcover. Estado de conservación: New. New HARDCOVER International Edition, Printed in Black and White, Only USPS Media mail Shipping ONLY, Different ISBN, Same Content As US edition, Book Cover may be Different, in English Language. Nº de ref. de la librería 24283

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Descripción Wileyand#8211;Blackwell, 2006. HRD. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería FW-9780470018750

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Descripción Wiley, 2007. Estado de conservación: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: Preface. Chapter 1 Introduction: The Bayesian Method, its Benefits and Implementation. Chapter 2 Bayesian Model Choice, Comparison and Checking. Chapter 3 The Major Densities and their Application. Chapter 4 Normal Linear Regression, General Linear Models and Log-Linear Models. Chapter 5 Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling. Chapter 6 Discrete Mixture Priors. Chapter 7 Multinomial and Ordinal Regression Models. Chapter 8 Time Series Models. Chapter 9 Modelling Spatial Dependencies. Chapter 10 Nonlinear and Nonparametric Regression. Chapter 11 Multilevel and Panel Data Models. Chapter 12 Latent Variable and Structural Equation Models for Multivariate Data. Chapter 13 Survival and Event History Analysis. Chapter 14 Missing Data Models. Chapter 15 Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations. Appendix 1 A Brief Guide to Using WINBUGS. Index. Nº de ref. de la librería ABE_book_new_0470018755

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Descripción John Wiley and Sons Ltd, United Kingdom, 2007. Hardback. Estado de conservación: New. 2nd Revised edition. 250 x 174 mm. Language: English . Brand New Book. Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition:* Provides an integrated presentation of theory, examples, applications and computer algorithms.* Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.* Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.* Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.* Provides exercises designed to help reinforce the reader s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains. - ISI - Short Book Reviews This is an excellent introductory book on Bayesian modelling techniques and data analysis - Biometrics The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods. - Journal of Mathematical Psychology. Nº de ref. de la librería AAH9780470018750

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Congdon, Peter
Editorial: John Wiley and Sons Ltd, United Kingdom (2007)
ISBN 10: 0470018755 ISBN 13: 9780470018750
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Descripción John Wiley and Sons Ltd, United Kingdom, 2007. Hardback. Estado de conservación: New. 2nd Revised edition. 250 x 174 mm. Language: English . Brand New Book. Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition:* Provides an integrated presentation of theory, examples, applications and computer algorithms.* Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.* Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.* Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.* Provides exercises designed to help reinforce the reader s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains. - ISI - Short Book Reviews This is an excellent introductory book on Bayesian modelling techniques and data analysis - Biometrics The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods. - Journal of Mathematical Psychology. Nº de ref. de la librería AAH9780470018750

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Congdon, Peter
Editorial: WILEY INDIA
ISBN 10: 0470018755 ISBN 13: 9780470018750
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Descripción WILEY INDIA. Estado de conservación: Brand New. This is Brand New International Edition, Paperback, Different ISBN, Same contents, Excellent Quality with Shrink Wrap, English language, Perfect condition. Please contact us for any questions regarding this book.!!. Nº de ref. de la librería TBZ-4613

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Congdon, Peter
Editorial: Somerset, New Jersey, U.S.A.: John Wiley & Sons Inc (2007)
ISBN 10: 0470018755 ISBN 13: 9780470018750
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Descripción Somerset, New Jersey, U.S.A.: John Wiley & Sons Inc, 2007. Hardcover. Estado de conservación: New. 2nd Edition. Ship out 1-2 business day,Brand new,US edition, Free tracking number usually 2-4 biz days delivery to worldwide Same shipping fee with US, Canada,Europe country, Australia, item will ship out from either LA or Asia,ht. Nº de ref. de la librería ABE-5396644584

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Editorial: Wiley (2007)
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Descripción Wiley, 2007. Hardcover. Estado de conservación: New. 2. Nº de ref. de la librería DADAX0470018755

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