• 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. Social networks - introduction
3. Why are marketers interested?
4. Issues in social network research
5. Our modeling focus and contribution
6. Nature of network data
7. Dyadic data
8. Structural aspects of network data
9. Network structure and heterogeneity
10. Statistical models of network structure
11. Model: three relationships
12. Directed and undirected relationships
13. Model: directed and valued relations
14. Correlated multivariate hurdle Poisson
15. The model
16. Model: dependence structure
17. Sender and receiver effects
18. Selectivity and reciprocity: valued relations
19. Latent Euclidean space for actors
20. Complete model
21. Conditional likelihood
22. Bayesian estimation: MCMC methods
23. Bayesian estimation: separation strategy prior
24. Application
25. Schema of a social network
26. Social networks
27. Swiss online social network of music artists
28. Independent variables
29. Density and reciprocity
30. Degree distribution - friendships
31. Degree distribution - communications
32. Degree distributions across relationships
33. 3 models
34. Posterior predictive distribution: friendship model
35. Model adequacy: posterior predictive checking
36. Predictive ability: music downloads
37. Parameter estimates: friendships
38. Parameter estimates: music downloads
39. Error covariance
40. Correlations in person effects
41. Takeaways
42. Future research

Topics Covered

• Social networks
• Some modeling questions and issues in social network research
• Nature of network data
• Network structure and heterogeneity
• Statistical models of network structure
• Sender and receiver effect
• Selectivity and reciprocity
• Bayesian estimation
• Schema of a social network
• Swiss online social network of music

Links

Series:

• Bayesian Analysis in Marketing

Categories:

• Marketing & Sales
• Technology & Operations

Talk Citation

Publication History

• Published on January 27, 2010