• 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. Review of Markov chains
3. Transition matrix
4. Stationary distribution (1)
5. Stationary distribution (2)
6. Time reversible chain (1)
7. Time reversible chain (2)
8. Stationary and time reversibility
9. Example
10. Principle number 1
11. Metropolis methods
12. Discrete case: Metropolis-Hastings algorithm (1)
13. Discrete case: Metropolis-Hastings algorithm (2)
14. Metropolis-Hastings algorithm - why do we repeat
15. Time reversible with respect to pie
16. Metropolis-Hastings algorithm example
17. Continuous Metropolis-Hastings
18. Principle number 2
19. Independence chain
20. Random walk (rw) chains
21. Independence vs. rw chains
22. Choosing a step size for the rw chain (1)
23. Relative numerical efficiency (1)
24. Relative numerical efficiency (2)
25. Choosing a step size for the rw chain (2)
26. The Gibbs sampler (1)
27. The Gibbs sampler (2)
28. Principle number 3
29. Logit model
30. Logit model-Hessian
31. Logit model MCMC algorithms
32. Scaling rw Metropolis
33. Comparison of indep / rw Metropolis
34. rmnlIndepMetrop (1)
35. rmnlIndepMetrop (2)
36. Time series of all the draws of beta
37. Quantile regression
38. Asymmetric Laplace distribution
39. Bayesian quantile regression
40. Quantile R code
41. Summary

Topics Covered

• Review of Markov chains
• Stationary distribution
• Time reversible chains
• Metropolis methods
• Discrete case: Metropolis-Hastings algorithm
• Independence chains
• Random walk chains
• Relative numerical efficiency
• The Gibbs sampler
• Logit model
• Scaling rw metropolis
• Quantile regression
• Asymmetric Laplace distribution

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Finance, Accounting & Economics
• Marketing & Sales

Talk Citation

Publication History

• Published on January 27, 2010