Fix dependence of Uniform logp on bound method

pymc3/distributions/continuous.py

@@ -268,7 +268,11 @@ def logp(self, value):
         """
         lower = self.lower
         upper = self.upper
-        return bound(-aet.log(upper - lower), value >= lower, value <= upper)
+        return bound(
+            aet.fill(value, -aet.log(upper - lower)),
+            value >= lower,
+            value <= upper,
+        )
 
     def logcdf(self, value):
         """

pymc3/distributions/discrete.py

@@ -1278,7 +1278,11 @@ def logp(self, value):
         """
         upper = self.upper
         lower = self.lower
-        return bound(-aet.log(upper - lower + 1), lower <= value, value <= upper)
+        return bound(
+            aet.fill(value, -aet.log(upper - lower + 1)),
+            lower <= value,
+            value <= upper,
+        )
 
     def logcdf(self, value):
         """

pymc3/distributions/dist_math.py

@@ -48,17 +48,23 @@
 def bound(logp, *conditions, **kwargs):
     """
     Bounds a log probability density with several conditions.
+    When conditions are not met, the logp values are replaced by -inf.
+
+    Note that bound should not be used to enforce the logic of the logp under the normal
+    support as it can be disabled by the user via check_bounds = False in pm.Model()
 
     Parameters
     ----------
     logp: float
     *conditions: booleans
     broadcast_conditions: bool (optional, default=True)
-        If True, broadcasts logp to match the largest shape of the conditions.
-        This is used e.g. in DiscreteUniform where logp is a scalar constant and the shape
-        is specified via the conditions.
-        If False, will return the same shape as logp.
-        This is used e.g. in Multinomial where broadcasting can lead to differences in the logp.
+        If True, conditions are broadcasted and applied element-wise to each value in logp.
+        If False, conditions are collapsed via aet.all(). As a consequence the entire logp
+        array is either replaced by -inf or unchanged.
+
+        Setting broadcasts_conditions to False is necessary for most (all?) multivariate
+        distributions where the dimensions of the conditions do not unambigously match
+        that of the logp.
 
     Returns
     -------

pymc3/distributions/multivariate.py

@@ -527,9 +527,9 @@ def logp(self, value):
         # only defined for sum(value) == 1
         return bound(
             aet.sum(logpow(value, a - 1) - gammaln(a), axis=-1) + gammaln(aet.sum(a, axis=-1)),
-            aet.all(value >= 0),
-            aet.all(value <= 1),
-            aet.all(a > 0),
+            value >= 0,
+            value <= 1,
+            a > 0,
             broadcast_conditions=False,
         )
 
@@ -671,11 +671,11 @@ def logp(self, x):
 
         return bound(
             factln(n) + aet.sum(-factln(x) + logpow(p, x), axis=-1, keepdims=True),
-            aet.all(x >= 0),
-            aet.all(aet.eq(aet.sum(x, axis=-1, keepdims=True), n)),
-            aet.all(p <= 1),
-            aet.all(aet.eq(aet.sum(p, axis=-1), 1)),
-            aet.all(aet.ge(n, 0)),
+            x >= 0,
+            aet.eq(aet.sum(x, axis=-1, keepdims=True), n),
+            p <= 1,
+            aet.eq(aet.sum(p, axis=-1), 1),
+            n >= 0,
             broadcast_conditions=False,
         )
 
@@ -823,10 +823,10 @@ def logp(self, value):
         # and that each observation value_i sums to n_i.
         return bound(
             result,
-            aet.all(aet.ge(value, 0)),
-            aet.all(aet.gt(a, 0)),
-            aet.all(aet.ge(n, 0)),
-            aet.all(aet.eq(value.sum(axis=-1, keepdims=True), n)),
+            value >= 0,
+            a > 0,
+            n >= 0,
+            aet.eq(value.sum(axis=-1, keepdims=True), n),
             broadcast_conditions=False,
         )
 
@@ -1575,8 +1575,8 @@ def logp(self, x):
         result += (eta - 1.0) * aet.log(det(X))
         return bound(
             result,
-            aet.all(X <= 1),
-            aet.all(X >= -1),
+            X >= -1,
+            X <= 1,
             matrix_pos_def(X),
             eta > 0,
             broadcast_conditions=False,
@@ -2204,9 +2204,10 @@ def logp(self, value):
         logquad = (self.tau * delta * tau_dot_delta).sum(axis=-1)
         return bound(
             0.5 * (logtau + logdet - logquad),
-            aet.all(self.alpha <= 1),
-            aet.all(self.alpha >= -1),
+            self.alpha >= -1,
+            self.alpha <= 1,
             self.tau > 0,
+            broadcast_conditions=False,
         )
 
     def random(self, point=None, size=None):

pymc3/model.py

@@ -817,7 +817,8 @@ class Model(Factor, WithMemoization, metaclass=ContextMeta):
         Ensure that input parameters to distributions are in a valid
         range. If your model is built in a way where you know your
         parameters can only take on valid values you can set this to
-        False for increased speed.
+        False for increased speed. This should not be used if your model
+        contains discrete variables.
 
     Examples
     --------

pymc3/tests/test_distributions.py

@@ -2667,6 +2667,17 @@ def test_issue_3051(self, dims, dist_cls, kwargs):
         assert actual_a.shape == (X.shape[0],)
         pass
 
+    def test_issue_4499(self):
+        # Test for bug in Uniform and DiscreteUniform logp when setting check_bounds = False
+        # https://github.com/pymc-devs/pymc3/issues/4499
+        with pm.Model(check_bounds=False) as m:
+            x = pm.Uniform("x", 0, 2, shape=10, transform=None)
+        assert_almost_equal(m.logp_array(np.ones(10)), -np.log(2) * 10)
+
+        with pm.Model(check_bounds=False) as m:
+            x = pm.DiscreteUniform("x", 0, 1, shape=10)
+        assert_almost_equal(m.logp_array(np.ones(10)), -np.log(2) * 10)
+
 
 def test_serialize_density_dist():
     def func(x):