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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
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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 December 7, 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