Alicia A. Johnson, Miles Q. Ott (Smith College, Northampton, MA 01063), Mine Dogucu (Denison university, OH, USA)

Bayes Rules!

An Introduction to Applied Bayesian Modeling

• Broschiertes Buch

Produktbeschreibung

This book 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. It empowers readers to weave Bayesian approaches into their everyday practice.

Produktdetails

• Chapman & Hall/CRC Texts in Statistical Science
• Verlag: Taylor & Francis Ltd
• Seitenzahl: 544
• Erscheinungstermin: 4. März 2022
• Englisch
• Abmessung: 256mm x 180mm x 32mm
• Gewicht: 1120g
• ISBN-13: 9780367255398
• ISBN-10: 0367255391
• Artikelnr.: 62571121

Autorenporträt

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.

Inhaltsangabe

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