diff --git a/pymc/model.py b/pymc/model.py
index aa6a8bfbad..7ffa5f6d74 100644
--- a/pymc/model.py
+++ b/pymc/model.py
@@ -900,10 +900,11 @@ def value_vars(self):
     @property
     def unobserved_value_vars(self):
         """List of all random variables (including untransformed projections),
-        as well as deterministics used as inputs and outputs of the the model's
+        as well as deterministics used as inputs and outputs of the model's
         log-likelihood graph
         """
         vars = []
+        untransformed_vars = []
         for rv in self.free_RVs:
             value_var = self.rvs_to_values[rv]
             transform = getattr(value_var.tag, "transform", None)
@@ -912,13 +913,16 @@ def unobserved_value_vars(self):
                 # each transformed variable
                 untrans_value_var = transform.backward(value_var, *rv.owner.inputs)
                 untrans_value_var.name = rv.name
-                vars.append(untrans_value_var)
+                untransformed_vars.append(untrans_value_var)
             vars.append(value_var)
 
+        # Remove rvs from untransformed values graph
+        untransformed_vars, _ = rvs_to_value_vars(untransformed_vars, apply_transforms=True)
+
         # Remove rvs from deterministics graph
         deterministics, _ = rvs_to_value_vars(self.deterministics, apply_transforms=True)
 
-        return vars + deterministics
+        return vars + untransformed_vars + deterministics
 
     @property
     def basic_RVs(self):
diff --git a/pymc/tests/test_sampling.py b/pymc/tests/test_sampling.py
index 7c8be684fa..d145631eb8 100644
--- a/pymc/tests/test_sampling.py
+++ b/pymc/tests/test_sampling.py
@@ -23,6 +23,7 @@
 import numpy as np
 import numpy.testing as npt
 import pytest
+import scipy.special
 
 from aesara import shared
 from arviz import InferenceData
@@ -313,6 +314,29 @@ def test_exceptions(self):
             with pytest.raises(NotImplementedError):
                 xvars = [t["mu"] for t in trace]
 
+    def test_deterministic_of_unobserved(self):
+        with pm.Model() as model:
+            x = pm.HalfNormal("x", 1)
+            y = pm.Deterministic("y", x + 100)
+            idata = pm.sample(
+                chains=1,
+                tune=10,
+                draws=50,
+                compute_convergence_checks=False,
+            )
+
+        np.testing.assert_allclose(idata.posterior["y"], idata.posterior["x"] + 100)
+
+    def test_transform_with_rv_depenency(self):
+        # Test that untransformed variables that depend on upstream variables are properly handled
+        with pm.Model() as m:
+            x = pm.HalfNormal("x", observed=1)
+            transform = pm.transforms.IntervalTransform(lambda *inputs: (inputs[-2], inputs[-1]))
+            y = pm.Uniform("y", lower=0, upper=x, transform=transform)
+            trace = pm.sample(tune=10, draws=50, return_inferencedata=False, random_seed=336)
+
+        assert np.allclose(scipy.special.expit(trace["y_interval__"]), trace["y"])
+
 
 def test_sample_find_MAP_does_not_modify_start():
     # see https://github.com/pymc-devs/pymc/pull/4458
@@ -1220,15 +1244,6 @@ def test_sample_from_xarray_posterior(self, point_list_arg_bug_fixture):
             pp = pm.sample_posterior_predictive(idat.posterior, var_names=["d"])
 
 
-def test_sample_deterministic():
-    with pm.Model() as model:
-        x = pm.HalfNormal("x", 1)
-        y = pm.Deterministic("y", x + 100)
-        idata = pm.sample(chains=1, draws=50, compute_convergence_checks=False)
-
-    np.testing.assert_allclose(idata.posterior["y"], idata.posterior["x"] + 100)
-
-
 class TestDraw(SeededTest):
     def test_univariate(self):
         with pm.Model():