Bayes Rules! : an Introduction to Applied Bayesian Modeling.

Format:

Book

Author/Creator:

Johnson, Alicia A., author.

Contributor:

Ott, Mies Q., editor.

Dogucu, Mine, editor.

ProQuest ebook central.

Class of 1932 Fund.

Series:

Chapman & Hall/CRC texts in statistical science.

Chapman & Hall/CRC texts in statistical science

Language:

English

Subjects (All):

Bayesian statistical decision theory.

R (Computer program language).

Physical Description:

1 online resource (552 pages).

polychrome

Edition:

First edition.

Place of Publication:

[Place of publication not identified] : Chapman and Hall/CRC, 2022.

System Details:

text file

Biography/History:

Alicia Johnson is an Associate Professor of Statistics at Macalester College in Saint Paul, Minnesota. She enjoys exploring and connecting students to Bayesian analysis, computational statistics, and the power of data in contributing to this shared world of ours. Miles Ott is a Senior Data Scientist at The Janssen Pharmaceutical Companies of Johnson & Johnson. Prior to his current position, he taught at Carleton College, Augsburg University, and Smith College. He is interested in biostatistics, LGBTQ+ health research, analysis of social network data, and statistics/data science education. He blogs at milesott.com and tweets about statistics, gardening, and his dogs on Twitter. Mine Dogucu is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. She spends majority of her time thinking about what to teach, how to teach it, and what tools to use while teaching. She likes intersectional feminism, cats, and R Ladies. She tweets about statistics and data science education on Twitter.

Contents:

List of TablesList of FiguresPrefaceAbout the AuthorChapter 1 The Big (Bayesian) PictureChapter 2 Bayes⁰́₉ RuleChapter 3 The Beta-Binomial Bayesian ModelChapter 4 Balance and Sequentiality in Bayesian AnalysesChapter 5 Conjugate Families Chapter 6 Approximating the PosteriorChapter 7 MCMC Under the HoodChapter 8 Posterior Inference and PredictionChapter 9 Simple Normal RegressionChapter 10 Evaluating Regression ModelsChapter 11 Extending the Normal Regression ModelChapter 12 Poisson and Negative Binomial RegressionChapter 13 Logistic RegressionChapter 14 Naive Bayes ClassificationChapter 15 Hierarchical Models are ExcitingChapter 16 (Normal) Hierarchical Models Without PredictorsChapter 17 (Normal) Hierarchical Models With PredictorsChapter 18 Non-Normal Hierarchical Regression & ClassificationChapter 19 Adding More Layers Bibliography Index

Notes:

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Vendor-supplied metadata.

Local Notes:

Acquired for the Penn Libraries with assistance from the Class of 1932 Fund.

ISBN:

9780429288340

0429288344

9781000529500

1000529509

9781000529562

1000529568

Publisher Number:

99990530262

Access Restriction:

Restricted for use by site license.