Bayesian Hierarchical Models and the Maximum Entropy Principle
Bayesian hierarchical models are frequently used in practical data analysis contexts. One interpretation of these models is that they provide an indirect way of assigning a prior for unknown parameters, through the introduction of hyperparameters. The resulting marginal prior for the parameters (integrating over the hyperparameters) is usually dependent, so that learning one parameter provides some information about the others. In this contribution, I will demonstrate that, when the prior given the hyperparameters is a canonical distribution (a maximum entropy distribution with moment constraints), the dependent marginal prior also has a maximum entropy property, with a different constrai...
| Authors | Brendon J. Brewer |
|---|---|
| Primary category | stat.ML |
| Published | 10 March 2026 |
| Last updated at source | 30 April 2026 |
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