Switching between class distributions

Questions
3

I have looked at the two links, but I think the problems solved are different from the one I am trying to solve.

  1. The number of classes is known and fixed (2 or 4 in my case)
  2. Per spectrum I know the label Y (to which class the spectrum belongs)

In the end, the goal is to introduce shared variables for Y and the spectral observations (x_s) so that (after inference) I can:

  1. set the variable Y=z and sample from the distribution z
  2. set a (unseen) spectrum x_s[a] and sample from the posterior distribution label, so I can calculate to which class the spectrum belongs.

An ideal solution would be something like this:

# labels (observed from the data, 0 or 1)
theta = pm.Beta('theta', 1., 1.)
label = pm.Bernoulli('label', p=theta, observed=Y)

sigma_e = pm.Gamma('sigma_e', alpha=1., beta=1.)
epsilon = pm.HalfNormal('epsilon', sd=sigma_e)

y_pred = pm.Normal('y_pred', mu=y_[label], sd=epsilon, observed=x_s)

In this way, whatever the label code is ([0|1] in this case, but [0|1|2|3] for 4 classes), but that gives the error:

`TypeError: list indices must be integers or slices, not ObservedRV`
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