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1. Introduction
2. Simulation methods
3. MCMC methods
4. The Markov chain
5. Ergodicity (1)
6. Practical considerations
7. Asymptotics
8. Simulating from Bivariate normal
9. Gibbs sampler (1)
10. Gibbs sampler (2)
11. Hammersley-Clifford theorem
12. rbiNormGibbs (1)
13. Intuition for dependence
14. rbiNormGibbs (2)
15. Ergodicity (2)
16. Relative numerical efficiency (1)
17. Relative numerical efficiency (2)
18. General Gibbs sampler
19. Different prior for Bayes regression
20. Different posterior
21. Different simulation strategy
22. runiregGibbs (1)
23. runiregGibbs (2)
24. runiregGibbs (3)
25. runiregGibbs (4)
26. R session (1)
27. R session (2)
28. R session (3)
29. Draws of beta
30. Draws of sigma squared
31. Summary

Topics Covered

• MCMC methods
• Ergodicity
• Gibbs sampler
• Relative numerical efficiency
• Bivariate normal example
• A different prior for Bayes regression

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on January 27, 2010