• 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. Unit-level data
3. Economic models
4. Solution - auxiliary function
5. Logit and probit models
6. Linear utility contours and budget
7. Choice probabilities
8. Logit choice probabilities
9. Principle number 1
10. Multinomial probit model
11. Differenced system
12. Estimating the differenced system
13. Identification problems in differenced system
14. Likelihood for mnp model
15. Conditional normal distribution
16. Drawing w
17. Identification issues
18. Principle number 2
19. Nested logit model
20. Outside goods
21. Superior and normal goods
22. Non-homothetic utility contours
23. Utility functions - demand for variety
24. Displacement parameters
25. Utility contours with diminishing marginal returns
26. Kuhn-Tucker conditions for satiation
27. Likelihood for satiation
28. Complements and attributes models
29. Principle number 3
30. Multivariate probit model
31. Estimating the multivariate probit model
32. Scotch data (1)
33. Scotch data (2)
34. Time series plot of normalized draws of beta
35. Scotch data - posterior marginal distributions
36. Scotch data - means and std. deviations
37. Component loadings
38. Summary

Topics Covered

• Economic models
• Logit and probit models
• Linear utility, contours and budget
• Choice probabilities
• Multinomial probit models
• Differenced system
• Conditional normal distribution
• Identification issues
• Superior and normal goods
• Displacement parameters
• Kuhn-Tucker conditions for satiation
• Estimating the multivariate probit model
• Component loadings

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on January 27, 2010