An Introductory Handbook of Bayesian Thinking

by Stephen C. Loftus

Overview

An Introductory Handbook of Bayesian Thinking brings Bayesian thinking and methods to a wide audience beyond the mathematical sciences. Appropriate for students with some background in calculus and introductory statistics, particularly for nonstatisticians with a sufficient mathematical background, the text provides a gentle introduction to Bayesian ideas with a wide array of supporting examples from a variety of fields. - Utilizes real datasets to illustrate Bayesian models and their results - Guides readers on coding Bayesian models using the statistical software R, including a helpful introduction and supporting online resource - Appropriate for an undergraduate statistics course, as well as for non-statisticians with sufficient mathematical background (integral and differential Calculus and an introductory Statistics course) - Covers any more advanced topics which readers may not be familiar with, such as the basic idea of vectors and matrices

Product Details

ISBN-13:	9780443291111
Publisher:	Elsevier Science & Technology Books
Publication date:	04/17/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	350
File size:	24 MB
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About the Author

Dr. Stephen Loftus is an Analyst in Research & Development for the Atlanta Braves. Prior to this, he held academic positions at Randolph-Macon College and Sweet Briar College. In his experience in academia and industry, Dr. Loftus has spent a great deal of time studying and developing Bayesian models for a variety of projects. These highly collaborative projects range from analysis in baseball to studies in numerical ecology. In developing these models, he found himself, on many occasions, needing to explain not only the decisions made in making these models, but also the rationale behind the Bayesian philosophy of statistics to individuals with diverse mathematical backgrounds.

Table of Contents

1. Probability and Random Variables2. Probability Distributions, Expected Value, and Variance3. Common Probability Distributions4. Conditional Probability and Bayes' Rule5. Finding and Using Distributions of Data6. Marginal and Conditional Distributions7. The Bayesian Switch8. A Brief Review of R9. Single Parameter Bayesian Inference10. Multi-Parameter Inference11. Gibbs Sampling in R12. Bayesian Linear Regression13. Bayesian Binary Regression14. Probabilistic Clustering15. Dealing with Non-conjugate Priors16. Models for Count Data17. Testing Hypotheses with Bayes18. Bayesian Inference Beyond This BookAppendix A: Matrix Form of Bayesian Linear RegressionAppendix B: Multivariate ClusteringAppendix C: List of Probability DistributionsAppendix D: Solutions to Practice Problems

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From the Publisher

Presents an accessible introduction to Bayesian ideas for the application of these valuable methods across fields