Share these talks and lectures with your colleagues
Invite colleaguesWe noted you are experiencing viewing problems
-
Check with your IT department that JWPlatform, JWPlayer and Amazon AWS & CloudFront are not being blocked by your network. The relevant domains are *.jwplatform.com, *.jwpsrv.com, *.jwpcdn.com, jwpltx.com, jwpsrv.a.ssl.fastly.net, *.amazonaws.com and *.cloudfront.net. The relevant ports are 80 and 443.
-
Check the following talk links to see which ones work correctly:
Auto Mode
HTTP Progressive Download Send us your results from the above test links at access@hstalks.com and we will contact you with further advice on troubleshooting your viewing problems. -
No luck yet? More tips for troubleshooting viewing issues
-
Contact HST Support access@hstalks.com
-
Please review our troubleshooting guide for tips and advice on resolving your viewing problems.
-
For additional help, please don't hesitate to contact HST support access@hstalks.com
We hope you have enjoyed this limited-length demo
This is a limited length demo talk; you may
login or
review methods of
obtaining more access.
Printable Handouts
Navigable Slide Index
- Introduction
- Censored regression models
- Hierarchical models
- Hierarchical models tell a hierarchical story
- Traditional estimation of hierarchical models
- Gibbs sampler for the probit model
- Probit conditional distributions (1)
- Inverse cdf method (1)
- Inverse cdf method (2)
- rtrun
- Probit conditional distributions (2)
- rbprobitGibbs (1)
- rbprobitGibbs (2)
- rbprobitGibbs (3)
- Basics of hierarchical models
- Conditional independence
- The hierarchical Bayes models
- Mixtures of normals
- Model hierarchy
- Gibbs sampler for mixture of normals (1)
- Gibbs sampler for mixture of normals (2)
- Multivariate mix of norms Ex (1)
- Multivariate mix of norms Ex (2)
- Data augmentation makes calculations easy
- Thank you
Topics Covered
- Fly fishing: a hierarchical model
- Motivation
- Traditional estimation
- Gibbs sampler for the probit model
- Probit conditional distributions
- Inverse cdf
- Basics of hierarchical models
- Conditional independence
- Mixtures of normals
- Model hierarchy
- Gibbs sampler for mixtures of normals
- Multivariate mix of norms Ex
Links
Series:
Categories:
Talk Citation
Allenby, G.M. (2010, January 27). Hierarchical models, conditional independence and data augmentation [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved November 21, 2024, from https://doi.org/10.69645/HECM2537.Export Citation (RIS)
Publication History
Hierarchical models, conditional independence and data augmentation
Published on January 27, 2010
53 min