Add logic to handle conditional nodes for observed variables

pymc/sampling/forward.py

@@ -345,10 +345,13 @@ def draw(
     return [np.stack(v) for v in drawn_values]
 
 
-def observed_dependent_deterministics(model: Model):
+def observed_dependent_deterministics(model: Model, extra_observeds=None):
     """Find deterministics that depend directly on observed variables."""
+    if extra_observeds is None:
+        extra_observeds = []
+
     deterministics = model.deterministics
-    observed_rvs = set(model.observed_RVs)
+    observed_rvs = set(model.observed_RVs + extra_observeds)
     blockers = model.basic_RVs
     return [
         deterministic
@@ -821,6 +824,7 @@ def sample_posterior_predictive(
         vars_ = model.observed_RVs + observed_dependent_deterministics(model)
         if observed_data is not None:
             vars_ += [model[x] for x in observed_data if x in model and x not in vars_]
+            vars_ += observed_dependent_deterministics(model, vars_)
 
     vars_to_sample = list(get_default_varnames(vars_, include_transformed=False))
 

tests/sampling/test_forward.py

@@ -561,7 +561,6 @@ def test_external_trace(self):
         )
         assert list(ppc.keys()) == ["a"]
 
-    @pytest.mark.xfail(reason="Auto-imputation of variables not supported in this setting")
     def test_external_trace_det(self):
         nchains = 2
         ndraws = 500
@@ -578,12 +577,12 @@ def test_external_trace_det(self):
         with pm.Model() as model_ppc:
             mu = pm.Normal("mu", 0.0, 1.0)
             a = pm.Normal("a", mu=mu, sigma=1)
-            b = pm.Deterministic("b", a + 1)
+            c = pm.Deterministic("c", a + 1)
 
         ppc = pm.sample_posterior_predictive(
             trace=trace, model=model_ppc, return_inferencedata=False
         )
-        assert list(ppc.keys()) == ["a", "b"]
+        assert list(ppc.keys()) == ["a", "c"]
 
     def test_normal_vector(self):
         with pm.Model() as model: