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It would be nice to illustrate the control over priors that users have - alpha, beta, and auxiliary parameters can all be set to different distributions with chosen parameters. Additionally, it is possible to have a hyperprior optimization example, maybe using GridSearchCV, to illustrate the available control and how to exploit it via scikit-learn interoperability.
The text was updated successfully, but these errors were encountered:
It would be nice to illustrate the control over priors that users have - alpha, beta, and auxiliary parameters can all be set to different distributions with chosen parameters. Additionally, it is possible to have a hyperprior optimization example, maybe using GridSearchCV, to illustrate the available control and how to exploit it via scikit-learn interoperability.
The text was updated successfully, but these errors were encountered: