Hi everyone,

I have a model that I can simplify to this:

```
beta_se = pm.HalfNormal('beta_se', 1.)
err_se = pm.HalfNormal('err_se', 1.)
beta= pm.Normal('beta', 0, beta_se, shape=(m, n))
y_pred_train = pm.Deterministic('y_pred_train ', tt.dot(X_train, beta))
y_pred_test = pm.Deterministic('y_pred_test ', tt.dot(X_test, beta))
##### not sure how to do this step:
if (y_pred_test >= y_test).all():
y_obs = pm.Normal('y_lik', mu=y_pred_train , sd=err_se, observed=y_train)
else:
likelihood = infinite
```

Basically: I’m only interested in the `B`

that cause my predictions of y in the prediction period to be greater than or equal to the true values of y_pred. Is there an idiomatic way to write this in pymc3? I can easily code this up in emcee - but - the chains I get take forever to converge and have a very poor autocorrelation.