Hierarchical Bayesian model with Geographic and temporal dimensions

Questions v5
2

I would try subsetting the beta “matrix” by group (row), i.e. (beta[indexed_states] * x).sum(axis=1). The expression x.dot(beta[indexed_states] might also work.

I was also struggling with this a while ago, cf. Unexpected difference in posterior estimates from two equivalent linear hierarchical models, fyi.

Unrelated: I found that pymc can work directly with pandas.DataFrame and Series objects so no need to call .values (if you want to convert use .to_numpy()).

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