Example library

Use these examples to follow a statistical problem from its assumptions to posterior interpretation. For the software basics, start with Model building, MCMC sampling, or Optimization.

• Overview
• GEV regression
• Compare samplers
• Variable selection with PyMC
• Correct measurement error

Example	Main question	Additional requirements
GEV regression	How can location, scale, and shape vary with covariates while respecting response support?	Liesel-GAM
Compare samplers	How do IWLS/Gibbs and NUTS/Gibbs behave on the same motorcycle regression model?	Liesel-GAM; bundled data with provenance
Variable selection with PyMC	How can Goose combine discrete indicator updates with continuous posterior sampling?	PyMC and its experimental Liesel interface
Correct measurement error	How can repeated noisy covariate measurements inform a latent-covariate regression?	Core Liesel

Each page contains its own setup and executes during the documentation build. The GEV and motorcycle examples are maintained here; follow their Liesel-GAM links for details of constructing additive terms. Reuse the canonical examples instead of maintaining a second copy in another package.

For a reproducible development environment and build command, see the documentation build instructions.