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.
1. An Introductory Handbook of Bayesian Thinking - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. v 7. About the author - p. ix 8. Preface - p. xi 9. Chapter 1 Probability and random variables - p. 3 10. Chapter 2 Probability distributions, expected value, and variance - p. 17 11. Chapter 3 Common probability distributions - p. 35 12. Chapter 4 Conditional probability and Bayes' Rule - p. 47 13. Chapter 5 Finding and using the distributions of data - p. 55 14. Chapter 6 Marginal and conditional distributions - p. 63 15. Chapter 7 The Bayesian switch - p. 71 16. Chapter 8 A brief review of R - p. 81 17. Chapter 9 Single-parameter inference - p. 95 18. Chapter 10 Two-parameter inference - p. 109 19. Chapter 11 Gibbs Sampling in R - p. 119 20. Chapter 12 Bayesian linear regression - p. 131 21. Chapter 13 Bayesian binary regression - p. 147 22. Chapter 14 Probabilistic clustering - p. 171 23. Chapter 15 Nonconjugate prior models - p. 187 24. Chapter 16 Models for count data - p. 205 25. Chapter 17 Testing hypotheses with Bayes - p. 227 26. Chapter 18 Bayesian inference beyond this book - p. 235 27. Appendix A Matrix-form of Bayesian linear regression - p. 243 28. Appendix B Multivariate clustering - p. 249 29. Appendix C List of probability distributions - p. 261 30. Appendix D Solutions to practice problems - p. 267 31. References - p. 295 32. Index - p. 299
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.
1. An Introductory Handbook of Bayesian Thinking - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Dedication - p. v 6. Table of Contents - p. v 7. About the author - p. ix 8. Preface - p. xi 9. Chapter 1 Probability and random variables - p. 3 10. Chapter 2 Probability distributions, expected value, and variance - p. 17 11. Chapter 3 Common probability distributions - p. 35 12. Chapter 4 Conditional probability and Bayes' Rule - p. 47 13. Chapter 5 Finding and using the distributions of data - p. 55 14. Chapter 6 Marginal and conditional distributions - p. 63 15. Chapter 7 The Bayesian switch - p. 71 16. Chapter 8 A brief review of R - p. 81 17. Chapter 9 Single-parameter inference - p. 95 18. Chapter 10 Two-parameter inference - p. 109 19. Chapter 11 Gibbs Sampling in R - p. 119 20. Chapter 12 Bayesian linear regression - p. 131 21. Chapter 13 Bayesian binary regression - p. 147 22. Chapter 14 Probabilistic clustering - p. 171 23. Chapter 15 Nonconjugate prior models - p. 187 24. Chapter 16 Models for count data - p. 205 25. Chapter 17 Testing hypotheses with Bayes - p. 227 26. Chapter 18 Bayesian inference beyond this book - p. 235 27. Appendix A Matrix-form of Bayesian linear regression - p. 243 28. Appendix B Multivariate clustering - p. 249 29. Appendix C List of probability distributions - p. 261 30. Appendix D Solutions to practice problems - p. 267 31. References - p. 295 32. Index - p. 299
