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Bayes rules! : an introduction to Bayesian modeling with R

Bayes rules! : an introduction to Bayesian modeling with R

Material type
단행본
Personal Author
Johnson, Alicia A. Ott, Miles Q., author. Dogucu, Mine, author.
Title Statement
Bayes rules! : an introduction to Bayesian modeling with R / Alicia A. Johnson, Miles Ott, Mine Dogucu.
Publication, Distribution, etc
Boca Raton :   CRC Press,   2022.  
Physical Medium
xxi, 521 p. : ill. (some col.) ; 27 cm.
Series Statement
Chapman & Hall/CRC texts in statistical science
ISBN
9780367255398 9781032191591
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Bayesian statistical decision theory. R (Computer program language).
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100 1 ▼a Johnson, Alicia A.
245 1 0 ▼a Bayes rules! : ▼b an introduction to Bayesian modeling with R / ▼c Alicia A. Johnson, Miles Ott, Mine Dogucu.
260 ▼a Boca Raton : ▼b CRC Press, ▼c 2022.
264 1 ▼a Boca Raton : ▼b CRC Press, ▼c 2022.
300 ▼a xxi, 521 p. : ▼b ill. (some col.) ; ▼c 27 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
490 1 ▼a Chapman & Hall/CRC texts in statistical science
504 ▼a Includes bibliographical references and index.
650 0 ▼a Bayesian statistical decision theory.
650 0 ▼a R (Computer program language).
700 1 ▼a Ott, Miles Q., ▼e author.
700 1 ▼a Dogucu, Mine, ▼e author.
830 0 ▼a Chapman and Hall/CRC texts in statistical science.
945 ▼a ITMT

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 519.542 J66b Accession No. 111875901 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

1 The Big (Bayesian) Picture 2 Bayes'' Rule 3 The Beta-Binomial Bayesian Model 4 Balance and Sequentiality in Bayesian Analyses 5 Conjugate Families 6 Approximating the Posterior 7 MCMC Under the Hood 8 Posterior Inference and Prediction 9 Simple Normal Regression 10 Evaluating Regression Models 11 Extending the Normal Regression Model 12 Poisson and Negative Binomial Regression 13 Logistic Regression 14 Naive Bayes Classification 15 Hierarchical Models are Exciting 16 (Normal) Hierarchical Models Without Predictors 17 (Normal) Hierarchical Models With Predictors 18 Non-Normal Hierarchical Regression & Classification 19 Adding More Layers

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