Stacking priors problem

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2

I don’t like responding to my own messages but I think, I solved the problem described above. For those who may come across similar issue it is worth taking a look at @junpenglao post here. I also struggled a bit with theano and shape parameter. There is fantastic description by @lucianopaz here. Finally the code that works for me is as follows:

     with pm.Model():
        priors = []
        for i, pri in enumerate(pri_inputs):
            if pri[0] == 1:
                priors.append(pm.Uniform("priors_{}".format(i), lower=pri[1], upper=pri[2]))
            elif pri[0] == 2:
                priors.append(pm.TruncatedNormal("priors_{}".format(i), mu=pri[1], sigma=pri[2], lower=pri[3], upper=pri[4]))

        priors = tt.stack(priors)[:, None]
    
        # Define the surrogate models for the outputs using the priors defined above
        linear = sm_linear * priors
        rsm = tt.sum(sm_rsm * priors * priors.T, axis=2)
        out_mu = sm_intercept + tt.sum(linear, axis=0) + tt.sum(rsm, axis=1)

        # Define loglikelihood as Multivariate Normal with defined covariance matrix and observations
        loglikelihood = pm.MvNormal("loglikelihood", mu=out_mu, cov=true_cov, observed=measured)

        # Inference
        step = pm.NUTS()  # using NUTS sampling
        trace = pm.sample(draws=samples, step=step, cores=2, progressbar=True)
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