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

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Location Call Number Status Location Service Notes
Lewis Library - Stacks QA279.5 .J64 2022 Browse related items Request

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    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.
    ISBN
    • 9780367255398 ((paperback))
    • 0367255391 ((paperback))
    • 9781032191591 ((hardcover))
    • 1032191597 ((hardcover))
    LCCN
    2021037969
    OCLC
    1268544586
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