Politics & Public Policy

Election analytics, multilevel models for survey data, constrained coefficients, and spatial voting models. Bayesian methods provide uncertainty quantification essential for policy decisions.

Election Analytics

1988 Election: Multilevel Modeling

Hierarchical logistic regression for voting behavior. Model individual vote choice with demographic fixed effects and state-level random intercepts. A classic Gelman & Hill example.

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Why Bayesian for Political Science?

Political data often has complex hierarchical structures (voters in districts in states) and requires principled uncertainty quantification:

Multilevel models handle clustered survey data correctly
Shrinkage estimation improves estimates for small states/districts
Full posterior inference enables probability statements like "P(candidate leads) = 73%"
Constrained coefficients enforce prior knowledge (e.g., non-negative effects)