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A First Course in Bayesian Statistical Methods (Springer Texts in Statistics...
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Características del artículo
- Estado
- Release Year
- 2009
- Book Title
- A First Course in Bayesian Statistical Methods (Springer Texts...
- ISBN
- 9780387922997
Acerca de este producto
Product Identifiers
Publisher
Springer New York
ISBN-10
0387922997
ISBN-13
9780387922997
eBay Product ID (ePID)
72344745
Product Key Features
Number of Pages
IX, 271 Pages
Language
English
Publication Name
First Course in Bayesian Statistical Methods
Publication Year
2009
Subject
Probability & Statistics / General, Operations Research, Management Science, Research, Statistics, Probability & Statistics / Bayesian Analysis
Type
Textbook
Subject Area
Mathematics, Social Science, Business & Economics
Series
Springer Texts in Statistics Ser.
Format
Hardcover
Dimensions
Item Weight
44.8 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2009-929120
Dewey Edition
22
TitleLeading
A
Reviews
From the reviews: This is an excellent book for its intended audience: statisticians who wish to learn Bayesian methods. Although designed for a statistics audience, it would also be a good book for econometricians who have been trained in frequentist methods, but wish to learn Bayes. In relatively few pages, it takes the reader through a vast amount of material, beginning with deep issues in statistical methodology such as de Finetti's theorem, through the nitty-gritty of Bayesian computation to sophisticated models such as generalized linear mixed effects models and copulas. And it does so in a simple manner, always drawing parallels and contrasts between Bayesian and frequentist methods, so as to allow the reader to see the similarities and differences with clarity. (Econometrics Journal) Generally, I think this is an excellent choice for a text for a one-semester Bayesian Course. It provides a good overview of the basic tenets of Bayesian thinking for the common one and two parameter distributions and gives introductions to Bayesian regression, multivariate-response modeling, hierarchical modeling, and mixed effects models. The book includes an ample collection of exercises for all the chapters. A strength of the book is its good discussion of Gibbs sampling and Metropolis-Hastings algorithms. The author goes beyond a description of the MCMC algorithms, but also provides insight into why the algorithms work. …I believe this text would be an excellent choice for my Bayesian class since it seems to cover a good number of introductory topics and giv the student a good introduction to the modern computational tools for Bayesian inference with illustrations using R. (Journal of the American Statistical Association, June 2010, Vol. 105, No. 490) Statisticians and applied scientists. The book is accessible to readers having a basic familiarity with probability theory and grounding statistical methods. The author has succeeded in writing an acceptable introduction to the theory and application of Bayesian statistical methods which is modern and covers both the theory and practice. … this book can be useful as a quick introduction to Bayesian methods for self study. In addition, I highly recommend this book as a text for a course for Bayesian statistics. (Lasse Koskinen, International Statistical Review, Vol. 78 (1), 2010) The book under review covers a balanced choice of topics … presented with a focus on the interplay between Bayesian thinking and the underlying mathematical concepts. … the book by Peter D. Hoff appears to be an excellent choice for a main reading in an introductory course. After studying this text the student can go in a direction of his liking at the graduate level. (Krzysztof atuszyski, Mathematical Reviews, Issue 2011 m) The book is a good introductory treatment of methods of Bayes analysis. It should especially appeal to the reader who has had some statistical courses in estimation and modeling, and wants to understand the Bayesian interpretation of those methods. Also, readers who are primarily interested in modeling data and who are working in areas outside of statistics should find this to be a good reference book. … should appeal to the reader who wants to keep with modern approaches to data analysis. (Richard P. Heydorn, Technometrics, Vol. 54 (1), February, 2012), From the reviews: This is an excellent book for its intended audience: statisticians who wish to learn Bayesian methods. Although designed for a statistics audience, it would also be a good book for econometricians who have been trained in frequentist methods, but wish to learn Bayes. In relatively few pages, it takes the reader through a vast amount of material, beginning with deep issues in statistical methodology such as de Finetti's theorem, through the nitty-gritty of Bayesian computation to sophisticated models such as generalized linear mixed effects models and copulas. And it does so in a simple manner, always drawing parallels and contrasts between Bayesian and frequentist methods, so as to allow the reader to see the similarities and differences with clarity. (Econometrics Journal) "Generally, I think this is an excellent choice for a text for a one-semester Bayesian Course. It provides a good overview of the basic tenets of Bayesian thinking for the common one and two parameter distributions and gives introductions to Bayesian regression, multivariate-response modeling, hierarchical modeling, and mixed effects models. The book includes an ample collection of exercises for all the chapters. A strength of the book is its good discussion of Gibbs sampling and Metropolis-Hastings algorithms. The author goes beyond a description of the MCMC algorithms, but also provides insight into why the algorithms work. ...I believe this text would be an excellent choice for my Bayesian class since it seems to cover a good number of introductory topics and giv the student a good introduction to the modern computational tools for Bayesian inference with illustrations using R. (Journal of the American Statistical Association, June 2010, Vol. 105, No. 490) "Statisticians and applied scientists. The book is accessible to readers having a basic familiarity with probability theory and grounding statistical methods. The author has succeeded in writing an acceptable introduction to the theory and application of Bayesian statistical methods which is modern and covers both the theory and practice. ... this book can be useful as a quick introduction to Bayesian methods for self study. In addition, I highly recommend this book as a text for a course for Bayesian statistics." (Lasse Koskinen, International Statistical Review, Vol. 78 (1), 2010) "The book under review covers a balanced choice of topics ... presented with a focus on the interplay between Bayesian thinking and the underlying mathematical concepts. ... the book by Peter D. Hoff appears to be an excellent choice for a main reading in an introductory course. After studying this text the student can go in a direction of his liking at the graduate level." (Krzysztof atuszyski, Mathematical Reviews, Issue 2011 m) "The book is a good introductory treatment of methods of Bayes analysis. It should especially appeal to the reader who has had some statistical courses in estimation and modeling, and wants to understand the Bayesian interpretation of those methods. Also, readers who are primarily interested in modeling data and who are working in areas outside of statistics should find this to be a good reference book. ... should appeal to the reader who wants to keep with modern approaches to data analysis." (Richard P. Heydorn, Technometrics, Vol. 54 (1), February, 2012), From the reviews:This is an excellent book for its intended audience: statisticians who wish to learn Bayesian methods. Although designed for a statistics audience, it would also be a good book for econometricians who have been trained in frequentist methods, but wish to learn Bayes. In relatively few pages, it takes the reader through a vast amount of material, beginning with deep issues in statistical methodology such as de Finetti's theorem, through the nitty-gritty of Bayesian computation to sophisticated models such as generalized linear mixed effects models and copulas. And it does so in a simple manner, always drawing parallels and contrasts between Bayesian and frequentist methods, so as to allow the reader to see the similarities and differences with clarity. (Econometrics Journal) Generally, I think this is an excellent choice for a text for a one-semester Bayesian Course. It provides a good overview of the basic tenets of Bayesian thinking for the common one and two parameter distributions and gives introductions to Bayesian regression, multivariate-response modeling, hierarchical modeling, and mixed effects models. The book includes an ample collection of exercises for all the chapters. A strength of the book is its good discussion of Gibbs sampling and Metropolis-Hastings algorithms. The author goes beyond a description of the MCMC algorithms, but also provides insight into why the algorithms work. …I believe this text would be an excellent choice for my Bayesian class since it seems to cover a good number of introductory topics and giv the student a good introduction to the modern computational tools for Bayesian inference with illustrations using R. (Journal of the American Statistical Association, June 2010, Vol. 105, No. 490)Statisticians and applied scientists. The book is accessible to readers having a basic familiarity with probability theory and grounding statistical methods. The author has succeeded in writing an acceptable introduction to the theory and application of Bayesian statistical methods which is modern and covers both the theory and practice. … this book can be useful as a quick introduction to Bayesian methods for self study. In addition, I highly recommend this book as a text for a course for Bayesian statistics. (Lasse Koskinen, International Statistical Review, Vol. 78 (1), 2010)The book under review covers a balanced choice of topics … presented with a focus on the interplay between Bayesian thinking and the underlying mathematical concepts. … the book by Peter D. Hoff appears to be an excellent choice for a main reading in an introductory course. After studying this text the student can go in a direction of his liking at the graduate level. (Krzysztof atuszyski, Mathematical Reviews, Issue 2011 m)
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
300.727
Table Of Content
and examples.- Belief, probability and exchangeability.- One-parameter models.- Monte Carlo approximation.- The normal model.- Posterior approximation with the Gibbs sampler.- The multivariate normal model.- Group comparisons and hierarchical modeling.- Linear regression.- Nonconjugate priors and Metropolis-Hastings algorithms.- Linear and generalized linear mixed effects models.- Latent variable methods for ordinal data.
Synopsis
A self-contained introduction to probability, exchangeability and Bayes' rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods., This compact, self-contained introduction to the theory and application of Bayesian statistical methods is accessible to those with a basic familiarity with probability, yet allows advanced readers to grasp the principles underlying Bayesian theory and method.
LC Classification Number
QA273.A1-274.9
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goodwillrs
99,3% de votos positivos•1,2 millones artículos vendidos
Registrado como vendedor profesional
Votos de vendedor (356.193)
- h***a (129)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaExceptional experience, start to finish!!! Excellent communication, shipping was fast, the "pre-owned" described item's condition including all of the original packaging to keep the lens safe! Item was as described, and a great value. Would buy aging from this seller.
- l***j (1792)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaHighly recommend this Goodwill seller the shipping was very quick received items in 5 days the packaging & value were good & item was as describedKiller Fame (Stella Knox FBI Mystery Series) (#167381607033)
- 2***o (84)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaOrdered cookbook late Sunday morning. Shipped early Monday morning. Arrived on time Wednesday late morning from within WI by USPS Bound Printed Matter in good condition shipped in a white exterior / gray interior plastic mailing envelope. Accurate description. Printed invoice included. Great value from a not-for-profit organization. Thanks!Taste of Home's 2003 Quick Cooking Annual Recipes by (2003) Hardcover (#396296720983)