Hierarchical model
A hierarchical model represents data or parameters at multiple nested levels, allowing information to be shared across related groups.
Effective Data Analysis
A hierarchical model represents data whose observations are nested within groups and estimates patterns at multiple levels.
- How it is used
- Frames nested observations as a modeling problem involving individual measurements and the groups that influence them.
- How deep it goes
- Defines the model class and illustrates its levels, but stops short of explaining estimation procedures or implementation details.
- Examples
- Uses subcontractors, employee departments, and students nested within classrooms, schools, and states.
- What you can do after
- Evaluate/understand when nested data require patterns to be modeled at both individual and group levels.
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Machine Learning Algorithms in Depth
A hierarchical regression model represents regression relationships across groups while sharing information through higher-level parameters.
- How it is used
- Models regression relationships across groups while sharing information through higher-level parameter distributions.
- How deep it goes
- Defines pooled, unpooled, and hierarchical structures, then implements Bayesian inference without detailing sampler internals.
- Examples
- Uses county-level radon measurements, basement status, shared Gaussian coefficient distributions, PyMC code, and MCMC traceplots.
- What you can do after
- Operate/configure a Bayesian hierarchical regression in PyMC and infer posterior distributions with NUTS.
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Related concepts
- probability distributionuses · A probability distribution is a mathematical specification assigning probabilities to the possible values or outcomes of a random variable, with total probability one.
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