How to make inference on new data with a hierarchical model?

[iavicenna]

Have you tried the suggestion here of also resetting the dimension of the model

"shape mismatch" when new data is set as a predictor for sample_posterior_predictive

With a simple linear regression, pymc.sample_posterior_predictive returns error: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (100,) and arg 1 with shape (20,). I used 100 data points as predictor for modeling, and set 20 data points as new predictor for predictions. Why does this happen? MacOS: BigSur (11.7) pymc: 4.4.0 Code is something like this: with pm.Model() as model: sigma = pm.HalfCauchy('sigma', beta=10) intercep…