Suppport for discrete max/min

pymc/logprob/order.py

@@ -111,16 +111,15 @@ def find_measurable_max(fgraph: FunctionGraph, node: Node) -> Optional[List[Tens
     if base_var.owner.op.dtype.startswith("int"):
         if isinstance(base_var.owner.op, RandomVariable):
             measurable_max = MeasurableMaxDiscrete(list(axis))
-            max_rv_node = measurable_max.make_node(base_var)
-            max_rv = max_rv_node.outputs
-
-            return max_rv
+        else:
+            return None
     else:
         measurable_max = MeasurableMax(list(axis))
-        max_rv_node = measurable_max.make_node(base_var)
-        max_rv = max_rv_node.outputs
 
-        return max_rv
+    max_rv_node = measurable_max.make_node(base_var)
+    max_rv = max_rv_node.outputs
+
+    return max_rv
 
 
 measurable_ir_rewrites_db.register(

tests/logprob/test_order.py

@@ -236,7 +236,7 @@ def test_min_non_mul_elemwise_fails():
 
 @pytest.mark.parametrize(
     "mu, size, value, axis",
-    [(2, 3, 0.85, -1), (2, 3, 0.01, 0), (1, 2, 0.2, None), (0, 4, 0, 0)],
+    [(2, 3, 0.85, -1), (2, 3, 1, 0), (1, 2, 2, None), (0, 4, 0, 0)],
 )
 def test_max_discrete(mu, size, value, axis):
     x = pm.Poisson.dist(name="x", mu=mu, size=(size))
@@ -247,8 +247,8 @@ def test_max_discrete(mu, size, value, axis):
     test_value = value
 
     n = size
-    exp_rv = np.exp(sp.poisson(mu).logcdf(test_value)) ** n
-    exp_rv_prev = np.exp(sp.poisson(mu).logcdf(test_value - 1)) ** n
+    exp_rv = sp.poisson(mu).cdf(test_value) ** n
+    exp_rv_prev = sp.poisson(mu).cdf(test_value - 1) ** n
 
     np.testing.assert_allclose(
         np.log(exp_rv - exp_rv_prev),