Get probability of parameter given new data

Questions
4

In that case, you can evaluate the likelihood of y belonging to each component, and then normalized the likelihood, something like:

ynew = np.asarray([.5])

complogp = y_obs.distribution._comp_logp(theano.shared(ynew))
f_complogp = model_mixed.model.fastfn(complogp)

weight_ynew = []
for ichain in range(trace_mixed.nchains):
    for point_idx in range(len(trace_mixed)):
        point  = trace_mixed._straces[ichain].point(point_idx)
        point.pop('sigma')
        point.pop('w')
        prob = np.exp(f_complogp(point))
        prob /= prob.sum()
        weight_ynew.append(prob)

weight_ynew = np.asarray(weight_ynew).squeeze()

sns.kdeplot(weight_ynew[:, 0]);
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