Model comparison for individual and combined datasets

Questions
7

Hi, I kind of have an opposite question. I fit a model hierarchically, sampling priors for all three parameters for all subjects from the same set of priors. I end up getting one waic score per model for all subjects for model comparison. I also hope to compare models for each individual subject. Is there a way for me to do it without having to fit the model for each individual subject again?

with pm.Model() as m:
      alpha = pm.Beta('alpha', alpha=1, beta=1, shape=n_subj)
      beta = pm.Gamma('beta', alpha=3, beta=1/2, shape=n_subj)
      decay = pm.Beta('decay', alpha=2, beta=15, shape=n_subj)
      param = at.as_tensor_variable([alpha, beta, decay], dtype='float64')
      like = pm.DensityDist('like', param, logp=agent.aesara_llik_td,observed=data_vec)