New
An Introductory Handbook of Bayesian Thinking,
Edition 1
By Stephen C. Loftus
Publication Date:
12 Aug 2024
Description
As Bayesian techniques become more common across a variety of fields, it becomes important for experts in those fields to understand those methods. 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.
Key Features
- 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—as much as needed in order to foster understanding of core concepts
About the author
By Stephen C. Loftus, Analyst, Research & Development, Atlanta Braves Baseball Club
Table of Contents
1. Probability and Random Variables
2. Probability Distributions, Expected Value, and Variance
3. Common Probability Distributions
4. Conditional Probability and Bayes' Rule
5. Finding and Using Distributions of Data
6. Marginal and Conditional Distributions
7. The Bayesian Switch
8. A Brief Review of R
9. Single Parameter Bayesian Inference
10. Multi-Parameter Inference
11. Gibbs Sampling in R
12. Bayesian Linear Regression
13. Bayesian Binary Regression
14. Probabilistic Clustering
15. Dealing with Non-conjugate Priors
16. Models for Count Data
17. Testing Hypotheses with Bayes
18. Bayesian Inference Beyond This Book
Appendix A: Matrix Form of Bayesian Linear Regression
Appendix B: Multivariate Clustering
Appendix C: List of Probability Distributions
Appendix D: Solutions to Practice Problems
2. Probability Distributions, Expected Value, and Variance
3. Common Probability Distributions
4. Conditional Probability and Bayes' Rule
5. Finding and Using Distributions of Data
6. Marginal and Conditional Distributions
7. The Bayesian Switch
8. A Brief Review of R
9. Single Parameter Bayesian Inference
10. Multi-Parameter Inference
11. Gibbs Sampling in R
12. Bayesian Linear Regression
13. Bayesian Binary Regression
14. Probabilistic Clustering
15. Dealing with Non-conjugate Priors
16. Models for Count Data
17. Testing Hypotheses with Bayes
18. Bayesian Inference Beyond This Book
Appendix A: Matrix Form of Bayesian Linear Regression
Appendix B: Multivariate Clustering
Appendix C: List of Probability Distributions
Appendix D: Solutions to Practice Problems
Book details
ISBN:
9780323954594
Page Count: 350
Retail Price
:
£56.95
9781439840955; 9781441928283
Instructor Resources
Access to teacher/student resources is available to registered users with approved inspection copies or confirmed adoptions. To review this material, please request an inspection copy.
Audience
Students in undergraduate programs learning about Bayesian Statistics Professionals / researchers / academics applying Bayesian principles in research and applied settings, who require an introduction or refresher to the subject
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