diff --git a/pymc/logprob/order.py b/pymc/logprob/order.py
index 2e4999d103..adb7c4e104 100644
--- a/pymc/logprob/order.py
+++ b/pymc/logprob/order.py
@@ -36,7 +36,6 @@
 
 from typing import List, Optional
 
-import pytensor
 import pytensor.tensor as pt
 
 from pytensor.graph.basic import Node
@@ -50,8 +49,6 @@
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.variable import TensorVariable
 
-import pymc as pm
-
 from pymc.logprob.abstract import (
     MeasurableVariable,
     _logcdf_helper,
@@ -109,7 +106,6 @@ def find_measurable_max(fgraph: FunctionGraph, node: Node) -> Optional[List[Tens
     if axis != base_var_dims:
         return None
 
-    # measurable_max = MeasurableMax(list(axis))
     # distinguish measurable discrete and continuous (because logprob is different)
     if base_var.owner.op.dtype.startswith("int"):
         measurable_max = MeasurableMaxDiscrete(list(axis))
@@ -147,6 +143,7 @@ def max_logprob(op, values, base_rv, **kwargs):
 @_logprob.register(MeasurableMaxDiscrete)
 def max_logprob_discrete(op, values, base_rv, **kwargs):
     r"""Compute the log-likelihood graph for the `Max` operation.
+
     The formula that we use here is :
     .. math::
         \ln(P_{(n)}(x)) = \ln(F(x)^n - F(x-1)^n)
@@ -180,7 +177,6 @@ class MeasurableMaxNegDiscrete(Max):
 
 @node_rewriter(tracks=[Max])
 def find_measurable_max_neg(fgraph: FunctionGraph, node: Node) -> Optional[List[TensorVariable]]:
-    # Add suppport for both graph
     rv_map_feature = getattr(fgraph, "preserve_rv_mappings", None)
 
     if rv_map_feature is None:
@@ -193,12 +189,10 @@ def find_measurable_max_neg(fgraph: FunctionGraph, node: Node) -> Optional[List[
 
     if base_var.owner is None:
         return None
-    # pytensor.dprint(node)
-    # if not rv_map_feature.request_measurable(node.inputs):
-    #     print("rv_map_feature.request_measurable(node.inputs) returns false")
-    #     return None
-    # print("If accepted")
-    # pytensor.dprint(node)
+
+    if not rv_map_feature.request_measurable(node.inputs):
+        return None
+
     # Min is the Max of the negation of the same distribution. Hence, op must be Elemwise
     if not isinstance(base_var.owner.op, Elemwise):
         return None
@@ -215,13 +209,12 @@ def find_measurable_max_neg(fgraph: FunctionGraph, node: Node) -> Optional[List[
         return None
 
     base_rv = base_var.owner.inputs[0]
-    # print(base_rv)
 
     # Non-univariate distributions and non-RVs must be rejected
     if not (isinstance(base_rv.owner.op, RandomVariable) and base_rv.owner.op.ndim_supp == 0):
         return None
 
-    if not rv_map_feature.request_measurable([base_rv]):
+    if isinstance(base_rv.owner.op, RandomVariable) and base_rv.owner.op.dtype.startswith("int"):
         return None
 
     # univariate i.i.d. test which also rules out other distributions
@@ -235,28 +228,18 @@ def find_measurable_max_neg(fgraph: FunctionGraph, node: Node) -> Optional[List[
     if axis != base_var_dims:
         return None
 
-    # distinguish measurable discrete and continuous (because logprob is different)
-    if base_rv.owner.op.dtype.startswith("int"):
-        if isinstance(base_rv.owner.op, RandomVariable):
-            measurable_min = MeasurableMaxNegDiscrete(list(axis))
-        else:
-            return None
-    else:
-        measurable_min = MeasurableMaxNeg(list(axis))
-
+    measurable_min = MeasurableMaxNeg(list(axis))
     min_rv_node = measurable_min.make_node(base_var)
     min_rv = min_rv_node.outputs
 
     return min_rv
 
 
-@node_rewriter(tracks=[pt.mul])
-def find_measurable_max_neg_rev(
+@node_rewriter(tracks=[Max])
+def find_measurable_max_neg_discrete(
     fgraph: FunctionGraph, node: Node
 ) -> Optional[List[TensorVariable]]:
-    # Add suppport for both graph
     rv_map_feature = getattr(fgraph, "preserve_rv_mappings", None)
-    pytensor.dprint(fgraph)
 
     if rv_map_feature is None:
         return None  # pragma: no cover
@@ -268,12 +251,7 @@ def find_measurable_max_neg_rev(
 
     if base_var.owner is None:
         return None
-    # pytensor.dprint(node)
-    # if not rv_map_feature.request_measurable(node.inputs):
-    #     print("rv_map_feature.request_measurable(node.inputs) returns false")
-    #     return None
-    # print("If accepted")
-    pytensor.dprint(node)
+
     # Min is the Max of the negation of the same distribution. Hence, op must be Elemwise
     if not isinstance(base_var.owner.op, Elemwise):
         return None
@@ -290,41 +268,36 @@ def find_measurable_max_neg_rev(
         return None
 
     base_rv = base_var.owner.inputs[1]
-    # print(base_rv)
 
     # Non-univariate distributions and non-RVs must be rejected
     if not (isinstance(base_rv.owner.op, RandomVariable) and base_rv.owner.op.ndim_supp == 0):
         return None
-    # print("a")
+
     if not rv_map_feature.request_measurable([base_rv]):
         return None
-    # print("b")
+
     # univariate i.i.d. test which also rules out other distributions
     for params in base_rv.owner.inputs[3:]:
         if params.type.ndim != 0:
             return None
-    # print("c")
+
     # Check whether axis is supported or not
     axis = set(node.op.axis)
     base_var_dims = set(range(base_var.ndim))
     if axis != base_var_dims:
         return None
-    # print("d")
+
     # distinguish measurable discrete and continuous (because logprob is different)
     if base_rv.owner.op.dtype.startswith("int"):
-        # print("g")
         if isinstance(base_rv.owner.op, RandomVariable):
-            # print("h")
             measurable_min = MeasurableMaxNegDiscrete(list(axis))
         else:
             return None
     else:
         measurable_min = MeasurableMaxNeg(list(axis))
-    # print("e")
+
     min_rv_node = measurable_min.make_node(base_var)
     min_rv = min_rv_node.outputs
-    # print("f")
-    # print(min_rv)
     return min_rv
 
 
@@ -335,11 +308,12 @@ def find_measurable_max_neg_rev(
     "min",
 )
 
+
 measurable_ir_rewrites_db.register(
-    "find_measurable_max_neg_rev",
-    find_measurable_max_neg_rev,
+    "find_measurable_max_neg_discrete",
+    find_measurable_max_neg_discrete,
     "basic",
-    "min",
+    "min_discrete",
 )
 
 
@@ -352,7 +326,7 @@ def max_neg_logprob(op, values, base_var, **kwargs):
     """
     (value,) = values
     base_rv = base_var.owner.inputs[0]
-    # print("in max neg")
+
     logprob = _logprob_helper(base_rv, -value)
     logcdf = _logcdf_helper(base_rv, -value)
 
@@ -374,9 +348,9 @@ def maxneg_logprob_discrete(op, values, base_rv, **kwargs):
     (value,) = values
     logcdf = _logcdf_helper(base_rv, value)
     logcdf_prev = _logcdf_helper(base_rv, value - 1)
-    # print("in discrete max neg")
+
     [n] = constant_fold([base_rv.size])
 
-    logprob = pm.logdiffexp(n * (1 - logcdf), n * (1 - logcdf_prev))
+    logprob = logdiffexp(n * logcdf_prev, n * logcdf)
 
     return logprob
diff --git a/pymc/logprob/transforms.py b/pymc/logprob/transforms.py
index fb5f4a0360..dd6fb756d1 100644
--- a/pymc/logprob/transforms.py
+++ b/pymc/logprob/transforms.py
@@ -40,7 +40,6 @@
 from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
-import pytensor
 import pytensor.tensor as pt
 
 from pytensor import scan
@@ -673,11 +672,7 @@ def find_measurable_transforms(fgraph: FunctionGraph, node: Node) -> Optional[Li
 
     # Do not apply rewrite to discrete variables
     if measurable_input.type.dtype.startswith("int"):
-        # print("a")
-        # print(node.op)
-        # print(isinstance(node.op, Mul))
         if str(node.op) != "Mul" and str(node.op) != "Add":
-            # print("b")
             return None
 
     # Check that other inputs are not potentially measurable, in which case this rewrite
@@ -961,9 +956,6 @@ def backward(self, value, *inputs):
 
     def log_jac_det(self, value, *inputs):
         scale = self.transform_args_fn(*inputs)
-        # print("d")
-        pytensor.dprint(scale)
-        pytensor.dprint(value.shape)
         return -pt.log(pt.abs(pt.broadcast_to(scale, value.shape)))
 
 
diff --git a/tests/logprob/test_order.py b/tests/logprob/test_order.py
index 5a44a4db1e..8a2135d8ed 100644
--- a/tests/logprob/test_order.py
+++ b/tests/logprob/test_order.py
@@ -221,7 +221,6 @@ def test_min_logprob(shape, value, axis):
         (x_min_logprob.eval({x_min_value: test_value})),
         rtol=1e-06,
     )
-    assert 0
 
 
 def test_min_non_mul_elemwise_fails():
@@ -255,27 +254,26 @@ def test_max_discrete(mu, size, value, axis):
         (x_max_logprob.eval({x_max_value: test_value})),
         rtol=1e-06,
     )
-    assert 0
-
-
-# @pytest.mark.parametrize(
-#     "mu, size, value, axis",
-#     [(2, 3, 0.85, -1), (2, 3, 1, 0), (1, 2, 2, None), (0, 4, 0, 0)],
-# )
-# def test_min_discrete(mu, size, value, axis):
-#     x = pm.Poisson.dist(name="x", mu=mu, size=(size))
-#     x_min = pt.min(x, axis=axis)
-#     x_min_value = pt.scalar("x_max_value")
-#     x_min_logprob = logp(x_min, x_min_value)
-
-#     test_value = value
-
-#     n = size
-#     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),
-#         (x_min_logprob.eval({x_min_value: test_value})),
-#         rtol=1e-06,
-#     )
+
+
+@pytest.mark.parametrize(
+    "mu, size, value, axis",
+    [(2, 3, 1, -1), (2, 3, 1, 0), (1, 2, 2, None), (0, 4, 0, 0)],
+)
+def test_min_discrete(mu, size, value, axis):
+    x = pm.Poisson.dist(name="x", mu=mu, size=(size))
+    x_min = pt.min(x, axis=axis)
+    x_min_value = pt.vector("x_min_value")
+    x_min_logprob = logp(x_min, x_min_value)
+
+    test_value = [value]
+
+    n = size
+    exp_rv = sp.poisson(mu).cdf(test_value[0]) ** n
+    exp_rv_prev = sp.poisson(mu).cdf(test_value[0] - 1) ** n
+
+    np.testing.assert_allclose(
+        (np.log(exp_rv_prev - exp_rv)),
+        (x_min_logprob.eval({x_min_value: (test_value)})),
+        rtol=1e-06,
+    )