= Hierarchical Bayesian model
A hierarchical Bayesian model assigns distributions to local <latent variables> conditional on shared <hyperparameters>, then distributions to observations conditional on those latent variables. Its <probabilistic graphical model> records this conditional factorization, often using a plate for repeated observations. Integrating out latent variables gives the marginal likelihood of the hyperparameters. Priors described as flat on logarithms require a <Jacobian determinant> when densities are written in the original positive variables.
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