• Fundamentals
• 34 min
  1. Bayesian essentials and bayesian regression

  • Prof. Peter Rossi
• 28 min
  2. Introduction to MCMC methods and the Gibbs sampler

  • Prof. Peter Rossi
• 53 min
  3. Hierarchical models, conditional independence and data augmentation

  • Prof. Greg M. Allenby
• 61 min
  4. Metropolis algorithms, logit and quantile regression estimation

  • Prof. Greg M. Allenby
• 52 min
  5. Unit-level models and discrete demand

  • Prof. Greg M. Allenby
• 65 min
  6. Heterogeneity

  • Prof. Greg M. Allenby
• 27 min
  7. Model choice and decision theory

  • Prof. Peter Rossi
• 28 min
  8. Bayesian instrumental variables and simultaneity

  • Prof. Peter Rossi
• Applications
• 43 min
  9. The value of HB in conjoint/choice analysis

  • Mr. Bryan K. Orme
• 41 min
  10. The SoV Probit

  • Dr. Jeff Brazell
• 56 min
  11. Trade-up and trade-down: models for capturing change in consideration set

  • Mr. Mark Garratt
• 37 min
  12. Bayesian modeling of social network data

  • Prof. Asim Ansari
• 52 min
  13. Joint choice decisions

  • Prof. Neeraj Arora
• 47 min
  14. Estimating an item's category role

  • Dr. Peter Boatwright
• 39 min
  15. Bayesian stochastic dynamic models for internet auctions

  • Prof. Eric T. Bradlow
• 42 min
  16. Hierarchical effects of advertising

  • Prof. Sandeep R. Chandukala
• 41 min
  17. Investigating the strategic influence of customer satisfaction on firm financial performance

  • Dr. Jeffrey Dotson
• 45 min
  18. A Bayesian approach to attribute based consideration sets

  • Prof. Timothy J. Gilbride
• 27 min
  19. Variety: models of multiple-discreteness

  • Prof. Jaehwan Kim
• 33 min
  20. Models for upper levels of a hierarchy

  • Dr. Qing Liu
• 75 min
  21. Making better pricing decisions with informative priors

  • Dr. Alan L. Montgomery
• 36 min
  22. Marketing mix modeling

  • Prof. Thomas Otter
• 45 min
  23. Reporting bias in survey data

  • Dr. Sha Yang

Printable Handouts

PDF

Navigable Slide Index

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