• 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. 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