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Navigable Slide Index

1. Introduction
2. The Goal of Inference
3. Data Aspects of Marketing Problems
4. The Likelihood Principle
5. Bayes Theorem
6. Summarizing the Posterior
7. Prediction (1)
8. Decision Theory
9. Is Sampling Theory Useful?
10. Sampling Properties of Bayes Estimators
11. Bayes Inference: Summary
12. Bayes and Classical Estimators (1)
13. Bayes and Classical Estimators (2)
14. Benefits and Costs of Bayes Inference
15. There’s No Theorem Like Bayes Theorem
16. Bayesian Computations
17. Conjugate Families
18. Beta-Binomial Model
19. Beta Distribution
20. Posterior (1)
21. Prediction (2)
22. Regression Model (1)
23. Regression Model (2)
24. Conjugate Prior
25. Geometry of Regression
26. Regression Likelihood (1)
27. Regression Likelihood (2)
28. Bayesian Regression
29. Posterior (2)
30. Simulating from Joint
31. IID Simulations (1)
32. IID Simulations (2)
33. Shrinkage and Conjugate Priors
34. Runireg (1)
35. Runireg (2)
36. Runireg: Output (1)
37. Runireg: Output (2)
38. Summary

Topics Covered

• The goal of inference
• Bayes theorem
• Decision theory
• Sampling properties of Bayes estimators
• Costs and benefits of Bayes
• Conjugate families
• Beta-binomial model
• Bayesian regression
• IID simulator
• Shrinkage and conjugate priors

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Finance, Accounting & Economics
• Marketing & Sales

Talk Citation

Publication History

• Published on January 27, 2010