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Hi!
I have a custom pymc model that I'd like to port to Bambi in the future. The model is of the form:
y ~ N(1 + treatment + row + col, s_y)
In this model we have a pattern like this to set priors:
spatial_variance_ratios = pm.Dirichlet( "spatial_variance_ratios", a=alphas, dims="sources" ) sigmas = {source: np.sqrt(spatial_variance_ratios[i]) for i, source in enumerate(sources)}
where sources = ["row", "col", "s_y"] and alphas is a list of the same size. Then later we would build the model with terms like this:
sources = ["row", "col", "s_y"]
alphas
predictor_row = pm.Normal("predictor_row", sigma=sigmas["row"], dims="row")
I was wondering if it would be possible to support defining priors similarly in Bambi. Thanks for your thoughts on this!
The text was updated successfully, but these errors were encountered:
Related to #584 and what is commented here as well #647.
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Hi!
I have a custom pymc model that I'd like to port to Bambi in the future. The model is of the form:
In this model we have a pattern like this to set priors:
where
sources = ["row", "col", "s_y"]
andalphas
is a list of the same size. Then later we would build the model with terms like this:I was wondering if it would be possible to support defining priors similarly in Bambi. Thanks for your thoughts on this!
The text was updated successfully, but these errors were encountered: