Bayes Rules!

An Introduction to Applied Bayesian Modeling
1. Auflage 2022
ISBN: 978-1-032-19159-1
Verlag: Chapman and Hall/CRC

Buch, Englisch, 544 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1206 g

Reihe: Chapman & Hall/CRC Texts in Statistical Science

An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. the book assumes that readers are familiar with the content covered in a typical undergraduate-level introductory statistics course. Readers will also, ideally, have some experience with undergraduate-level probability, calculus, and the R statistical software. Readers without this background will still be able to follow along so long as they
are eager to pick up these tools on the fly as all R code is provided.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.

Features

• Utilizes data-driven examples and exercises.

• Emphasizes the iterative model building and evaluation process.

• Surveys an interconnected range of multivariable regression and classification models.

• Presents fundamental Markov chain Monte Carlo simulation.

• Integrates R code, including RStan modeling tools and the bayesrules package.

• Encourages readers to tap into their intuition and learn by doing.

• Provides a friendly and inclusive introduction to technical Bayesian concepts.

• Supports Bayesian applications with foundational Bayesian theory.

Autoren/Hrsg.

Johnson, Alicia A.

Ott, Miles Q.

Dogucu, Mine

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz

Inhaltsverzeichnis

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

Über Autor(innen)

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.