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.
