diff --git a/pymc/distributions/timeseries.py b/pymc/distributions/timeseries.py
index 6469cd101..14fcd1ec8 100644
--- a/pymc/distributions/timeseries.py
+++ b/pymc/distributions/timeseries.py
@@ -21,7 +21,7 @@
 import pytensor
 import pytensor.tensor as pt
 
-from pytensor.graph.basic import Node, ancestors
+from pytensor.graph.basic import Apply, ancestors
 from pytensor.graph.replace import clone_replace
 from pytensor.tensor import TensorVariable
 from pytensor.tensor.random.op import RandomVariable
@@ -490,7 +490,7 @@ def step(*args):
             constant_term=constant_term,
         )(rhos, sigma, init_dist, steps, noise_rng)
 
-    def update(self, node: Node):
+    def update(self, node: Apply):
         """Return the update mapping for the noise RV."""
         return {node.inputs[-1]: node.outputs[0]}
 
@@ -767,7 +767,7 @@ def step(prev_y, prev_sigma, omega, alpha_1, beta_1, rng):
             outputs=[noise_next_rng, garch11],
         )(omega, alpha_1, beta_1, initial_vol, init_dist, steps, noise_rng)
 
-    def update(self, node: Node):
+    def update(self, node: Apply):
         """Return the update mapping for the noise RV."""
         return {node.inputs[-1]: node.outputs[0]}
 
@@ -918,7 +918,7 @@ def step(*prev_args):
             extended_signature=f"(),(s),{','.join('()' for _ in sde_pars)},[rng]->[rng],(t)",
         )(init_dist, steps, *sde_pars, noise_rng)
 
-    def update(self, node: Node):
+    def update(self, node: Apply):
         """Return the update mapping for the noise RV."""
         return {node.inputs[-1]: node.outputs[0]}
 
diff --git a/pymc/distributions/truncated.py b/pymc/distributions/truncated.py
index 6f32918bb..36b439526 100644
--- a/pymc/distributions/truncated.py
+++ b/pymc/distributions/truncated.py
@@ -19,7 +19,7 @@
 
 from pytensor import config, graph_replace, scan
 from pytensor.graph import Op
-from pytensor.graph.basic import Node
+from pytensor.graph.basic import Apply
 from pytensor.raise_op import CheckAndRaise
 from pytensor.scan import until
 from pytensor.tensor import TensorConstant, TensorVariable
@@ -211,7 +211,7 @@ def _create_logcdf_exprs(
         upper_logcdf = graph_replace(lower_logcdf, {lower_value: upper_value})
         return lower_logcdf, upper_logcdf
 
-    def update(self, node: Node):
+    def update(self, node: Apply):
         """Return the update mapping for the internal RNGs.
 
         TruncatedRVs are created in a way that the rng updates follow the same order as the input RNGs.
diff --git a/pymc/logprob/binary.py b/pymc/logprob/binary.py
index 0767d25f8..9d0985a2c 100644
--- a/pymc/logprob/binary.py
+++ b/pymc/logprob/binary.py
@@ -11,11 +11,12 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
+from typing import cast
 
 import numpy as np
 import pytensor.tensor as pt
 
-from pytensor.graph.basic import Node
+from pytensor.graph.basic import Apply
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.rewriting.basic import node_rewriter
 from pytensor.scalar.basic import GE, GT, LE, LT, Invert
@@ -39,7 +40,7 @@ class MeasurableComparison(MeasurableElemwise):
 
 
 @node_rewriter(tracks=[gt, lt, ge, le])
-def find_measurable_comparisons(fgraph: FunctionGraph, node: Node) -> list[TensorVariable] | None:
+def find_measurable_comparisons(fgraph: FunctionGraph, node: Apply) -> list[TensorVariable] | None:
     measurable_inputs = filter_measurable_variables(node.inputs)
 
     if len(measurable_inputs) != 1:
@@ -55,7 +56,7 @@ def find_measurable_comparisons(fgraph: FunctionGraph, node: Node) -> list[Tenso
 
     # Check that the other input is not potentially measurable, in which case this rewrite
     # would be invalid
-    const = node.inputs[(measurable_var_idx + 1) % 2]
+    const = cast(TensorVariable, node.inputs[(measurable_var_idx + 1) % 2])
 
     # check for potential measurability of const
     if check_potential_measurability([const]):
@@ -127,11 +128,11 @@ class MeasurableBitwise(MeasurableElemwise):
 
 
 @node_rewriter(tracks=[invert])
-def find_measurable_bitwise(fgraph: FunctionGraph, node: Node) -> list[TensorVariable] | None:
-    base_var = node.inputs[0]
+def find_measurable_bitwise(fgraph: FunctionGraph, node: Apply) -> list[TensorVariable] | None:
+    base_var = cast(TensorVariable, node.inputs[0])
 
     if not base_var.dtype.startswith("bool"):
-        raise None
+        return None
 
     if not filter_measurable_variables([base_var]):
         return None
diff --git a/pymc/logprob/censoring.py b/pymc/logprob/censoring.py
index 2104ecb6e..e17d30a43 100644
--- a/pymc/logprob/censoring.py
+++ b/pymc/logprob/censoring.py
@@ -38,7 +38,7 @@
 import numpy as np
 import pytensor.tensor as pt
 
-from pytensor.graph.basic import Node
+from pytensor.graph.basic import Apply
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.rewriting.basic import node_rewriter
 from pytensor.scalar.basic import Ceil, Clip, Floor, RoundHalfToEven
@@ -62,7 +62,7 @@ class MeasurableClip(MeasurableElemwise):
 
 
 @node_rewriter(tracks=[clip])
-def find_measurable_clips(fgraph: FunctionGraph, node: Node) -> list[TensorVariable] | None:
+def find_measurable_clips(fgraph: FunctionGraph, node: Apply) -> list[TensorVariable] | None:
     # TODO: Canonicalize x[x>ub] = ub -> clip(x, x, ub)
 
     if not filter_measurable_variables(node.inputs):
@@ -153,7 +153,7 @@ class MeasurableRound(MeasurableElemwise):
 
 
 @node_rewriter(tracks=[ceil, floor, round_half_to_even])
-def find_measurable_roundings(fgraph: FunctionGraph, node: Node) -> list[TensorVariable] | None:
+def find_measurable_roundings(fgraph: FunctionGraph, node: Apply) -> list[TensorVariable] | None:
     if not filter_measurable_variables(node.inputs):
         return None
 
diff --git a/pymc/logprob/transforms.py b/pymc/logprob/transforms.py
index 41233223b..930bf1f4e 100644
--- a/pymc/logprob/transforms.py
+++ b/pymc/logprob/transforms.py
@@ -42,7 +42,7 @@
 
 from pytensor import scan
 from pytensor.gradient import jacobian
-from pytensor.graph.basic import Node, Variable
+from pytensor.graph.basic import Apply, Variable
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.rewriting.basic import node_rewriter
 from pytensor.scalar import (
@@ -453,7 +453,7 @@ def measurable_power_exponent_to_exp(fgraph, node):
         erfcx,
     ]
 )
-def find_measurable_transforms(fgraph: FunctionGraph, node: Node) -> list[Node] | None:
+def find_measurable_transforms(fgraph: FunctionGraph, node: Apply) -> list[Variable] | None:
     """Find measurable transformations from Elemwise operators."""
     # Node was already converted
     if isinstance(node.op, MeasurableOp):
diff --git a/pymc/step_methods/metropolis.py b/pymc/step_methods/metropolis.py
index d825c8857..a7c7a36c6 100644
--- a/pymc/step_methods/metropolis.py
+++ b/pymc/step_methods/metropolis.py
@@ -59,7 +59,7 @@
     "MultivariateNormalProposal",
 ]
 
-from pymc.util import get_value_vars_from_user_vars
+from pymc.util import RandomGenerator, get_value_vars_from_user_vars
 
 # Available proposal distributions for Metropolis
 
@@ -302,7 +302,7 @@ def astep(self, q0: RaveledVars) -> tuple[RaveledVars, StatsType]:
             accept_rate = self.delta_logp(q, q0d)
             q, accepted = metrop_select(accept_rate, q, q0d, rng=self.rng)
             self.accept_rate_iter = accept_rate
-            self.accepted_iter = accepted
+            self.accepted_iter[0] = accepted
             self.accepted_sum += accepted
 
         self.steps_until_tune -= 1
@@ -622,14 +622,16 @@ class CategoricalGibbsMetropolis(ArrayStep):
 
     _state_class = CategoricalGibbsMetropolisState
 
-    def __init__(self, vars, proposal="uniform", order="random", model=None, rng=None):
+    def __init__(
+        self, vars, proposal="uniform", order="random", model=None, rng: RandomGenerator = None
+    ):
         model = pm.modelcontext(model)
 
         vars = get_value_vars_from_user_vars(vars, model)
 
         initial_point = model.initial_point()
 
-        dimcats = []
+        dimcats: list[tuple[int, int]] = []
         # The above variable is a list of pairs (aggregate dimension, number
         # of categories). For example, if vars = [x, y] with x being a 2-D
         # variable with M categories and y being a 3-D variable with N
@@ -665,10 +667,10 @@ def __init__(self, vars, proposal="uniform", order="random", model=None, rng=Non
             self.dimcats = [dimcats[j] for j in order]
 
         if proposal == "uniform":
-            self.astep = self.astep_unif
+            self.astep = self.astep_unif  # type: ignore[assignment]
         elif proposal == "proportional":
             # Use the optimized "Metropolized Gibbs Sampler" described in Liu96.
-            self.astep = self.astep_prop
+            self.astep = self.astep_prop  # type: ignore[assignment]
         else:
             raise ValueError("Argument 'proposal' should either be 'uniform' or 'proportional'")
 
diff --git a/scripts/run_mypy.py b/scripts/run_mypy.py
index 842fb0a13..ccce06736 100755
--- a/scripts/run_mypy.py
+++ b/scripts/run_mypy.py
@@ -32,8 +32,6 @@
 pymc/distributions/timeseries.py
 pymc/distributions/truncated.py
 pymc/initial_point.py
-pymc/logprob/binary.py
-pymc/logprob/censoring.py
 pymc/logprob/basic.py
 pymc/logprob/mixture.py
 pymc/logprob/rewriting.py