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In a multiple Bayesian linear regression model, do all variables (dependent and predictors) get prior distributions?

If so, can one mix non-informative and substantive priors in the model?

Thanks!

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In a multiple Bayesian linear regression model, do all variables (dependent and predictors) get prior distributions?

All parameters get a prior. Random variables/data might get a prior if you're modeling missing/latent/hidden data.

If so, can one mix non-informative and substantive priors in the model?

Based on this information alone, I don't see anything wrong with this approach.

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