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Patterns for hierarchical/multilevel Bayesian models including random effects, partial pooling, and centered vs non-centered parameterizations.
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Licença: MIT
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Hierarchical Models
When to Use
- Nested/grouped data (students in schools, patients in hospitals)
- Repeated measurements on subjects
- Meta-analysis with study-level variation
- Partial pooling between complete pooling and no pooling
Core Concept: Partial Pooling
Group means shrink toward overall mean based on:
- Within-group sample size
- Within-group variance
- Between-group variance
Stan Implementation
Centered Parameterization (Default)
data {
int<low
[Description truncada. Veja o README completo no GitHub.]
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