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A First Course in Bayesian Statistical Methods by Peter D Hoff: New
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Specifiche dell'oggetto
- Condizione
- Book Title
- A First Course in Bayesian Statistical Methods
- Publication Date
- 2009-06-15
- Pages
- 271
- ISBN
- 9780387922997
Informazioni su questo prodotto
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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AlibrisBooks
98,5% di Feedback positivi•1,9 milioni oggetti venduti
Registrato come venditore professionale
Feedback sul venditore (502.595)
- a***a (346)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoThis hardback book is of the highest quality, has a fine appearance , arrived in perfect condition, and is an excellent value. On what I was not asked about this time, communicating with the seller would have required using email outside of the eBay system, because they do not accept eBay messages, the book was well packed in a purpose-designed cardboard box, the shipping was faster than I expected for the bound media rate, and the book was exactly as described and pictured.Tameshigiri - The History and Development of Japanese Sword Testing by Sesko (N° 282906424466)
- r***w (213)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoItem was lost in delivery- but after some communication, seller was understanding and provided a refund. I ordered another and had it sent elsewhere. My main issue was that it felt as if I was getting automated responses at first!! Please have humans review human inquiries!! It was painstaking explaining the situation to an A.I. who only repeats unhelpful replies. Thanks again!! Otherwise. Great price! Great seller!
- a***o (684)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoBook is like new as described (remainder). Price was fair Shipping time was quick, but one problem is the packaging. It was simply put in a big brown envelope (too big for the mailbox) with no inner packing i.e no plastic wrapping inside to protect the book. The book was left outside our door in the rain while we were out and the envelope was soaked. When we dried the package and opened it the back of the dust jacket was wet. I suggest the seller use some inner package packing to the protect it