Testing a fix by truncating cdf to precision.

pymc/testing.py

@@ -699,13 +699,21 @@ def check_selfconsistency_discrete_icdf(
     distribution: Distribution,
     domain: Domain,
     paramdomains: Dict[str, Domain],
+    decimal: Optional[int] = None,
     n_samples: int = 100,
 ) -> None:
     """
     Check that the icdf and logcdf functions of the distribution are
     consistent for a sample of values in the domain of the
     distribution.
     """
+
+    def ftrunc(values, decimal=0):
+        return np.trunc(values * 10**decimal) / (10**decimal)
+
+    if decimal is None:
+        decimal = select_by_precision(float64=6, float32=3)
+
     dist = create_dist_from_paramdomains(distribution, paramdomains)
 
     value = pt.TensorType(dtype="float64", shape=[])("value")
@@ -726,7 +734,7 @@ def check_selfconsistency_discrete_icdf(
         with pytensor.config.change_flags(mode=Mode("py")):
             expected_value = value
             computed_value = dist_icdf_fn(
-                **point, value=np.exp(dist_logcdf_fn(**point, value=value))
+                **point, value=ftrunc(np.exp(dist_logcdf_fn(**point, value=value)), decimal=decimal)
             )
             npt.assert_almost_equal(
                 expected_value,