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Bayes rules! : an introduction to applied Bayesian modeling / Alicia A. Johnson, Miles Q. Ott, Mine Dogucu.
Author
Johnson, Alicia A.
[Browse]
Format
Book
Language
English
Εdition
First edition.
Published/Created
Boca Raton : CRC Press, 2022.
Description
xxi, 521 pages : illustrations (some color) ; 27 cm.
Availability
Copies in the Library
Location
Call Number
Status
Location Service
Notes
Lewis Library - Stacks
QA279.5 .J64 2022
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Details
Subject(s)
Bayesian statistical decision theory
[Browse]
R (Computer program language)
[Browse]
Author
Ott, Miles Q.
[Browse]
Dogucu, Mine
[Browse]
Series
Texts in statistical science
[More in this series]
Chapman & Hall/CRC texts in statistical science series
Summary note
"An engaging, sophisticated, and fun introduction to the field of Bayesian Statistics, Bayes Rules! An Introduction to Bayesian Modeling with R brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, it is an ideal resource for advanced undergraduate Statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum"-- Provided by publisher.
Notes
"A Chapman & Hall Book."
Bibliographic references
Includes bibliographical references and index.
Contents
Bayesian foundations. The big (Bayesian) picture – Baye’s rule
The beta-binomial Bayesian model
Balance and sequentiality in Bayesian analyses
Conjugate families – Posterior simulation & analysis. Approximating the posterior
MCMC Under the hood
Posterior inference & prediction – Bayesian regression & classification. Simple normal regression
Evaluating regression models
Extending the normal regression model
Poisson & negative binomial regression
Logistic regression
Naive Bayes classification
Hierarchical Bayesian models. Hierarchical models are exciting
(Normal) hierarchical models without predictors
(Normal) hierarchical models with predictors
Non-normal hierarchical regression & classification
Adding more layers.
Show 13 more Contents items
ISBN
9780367255398 ((paperback))
0367255391 ((paperback))
9781032191591 ((hardcover))
1032191597 ((hardcover))
LCCN
2021037969
OCLC
1268544586
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