diff --git a/pymc/distributions/distribution.py b/pymc/distributions/distribution.py
index 51ec6ebcf1..0384f90470 100644
--- a/pymc/distributions/distribution.py
+++ b/pymc/distributions/distribution.py
@@ -50,7 +50,7 @@
     rv_size_is_none,
     shape_from_dims,
 )
-from pymc.logprob.abstract import MeasurableVariable, _icdf, _logcdf, _logprob
+from pymc.logprob.abstract import MeasurableOp, _icdf, _logcdf, _logprob
 from pymc.logprob.basic import logp
 from pymc.logprob.rewriting import logprob_rewrites_db
 from pymc.printing import str_for_dist
@@ -228,7 +228,7 @@ def __get__(self, instance, type_):
         return descr_get(instance, type_)
 
 
-class SymbolicRandomVariable(OpFromGraph):
+class SymbolicRandomVariable(MeasurableOp, OpFromGraph):
     """Symbolic Random Variable
 
     This is a subclasse of `OpFromGraph` which is used to encapsulate the symbolic
@@ -624,10 +624,6 @@ def dist(
         return rv_out
 
 
-# Let PyMC know that the SymbolicRandomVariable has a logprob.
-MeasurableVariable.register(SymbolicRandomVariable)
-
-
 @node_rewriter([SymbolicRandomVariable])
 def inline_symbolic_random_variable(fgraph, node):
     """
diff --git a/pymc/logprob/abstract.py b/pymc/logprob/abstract.py
index 41c92e422d..38d06505d0 100644
--- a/pymc/logprob/abstract.py
+++ b/pymc/logprob/abstract.py
@@ -35,6 +35,7 @@
 #   SOFTWARE.
 
 import abc
+import warnings
 
 from collections.abc import Sequence
 from functools import singledispatch
@@ -46,6 +47,17 @@
 from pytensor.tensor.random.op import RandomVariable
 
 
+def __getattr__(name):
+    if name == "MeasurableVariable":
+        warnings.warn(
+            f"{name} has been deprecated in favor of MeasurableOp. Importing will fail in a future release.",
+            FutureWarning,
+        )
+        return MeasurableOpMixin
+
+    raise AttributeError(f"module {__name__} has no attribute {name}")
+
+
 @singledispatch
 def _logprob(
     op: Op,
@@ -131,14 +143,21 @@ def _icdf_helper(rv, value, **kwargs):
     return rv_icdf
 
 
-class MeasurableVariable(abc.ABC):
-    """A variable that can be assigned a measure/log-probability"""
+class MeasurableOp(abc.ABC):
+    """An operation whose outputs can be assigned a measure/log-probability"""
+
 
+MeasurableOp.register(RandomVariable)
 
-MeasurableVariable.register(RandomVariable)
 
+class MeasurableOpMixin(MeasurableOp):
+    """MeasurableOp Mixin with a distinctive string representation"""
 
-class MeasurableElemwise(Elemwise):
+    def __str__(self):
+        return f"Measurable{super().__str__()}"
+
+
+class MeasurableElemwise(MeasurableOpMixin, Elemwise):
     """Base class for Measurable Elemwise variables"""
 
     valid_scalar_types: tuple[MetaType, ...] = ()
@@ -150,6 +169,3 @@ def __init__(self, scalar_op, *args, **kwargs):
                 f"Acceptable types are {self.valid_scalar_types}"
             )
         super().__init__(scalar_op, *args, **kwargs)
-
-
-MeasurableVariable.register(MeasurableElemwise)
diff --git a/pymc/logprob/basic.py b/pymc/logprob/basic.py
index 3cf91479e8..c945baa751 100644
--- a/pymc/logprob/basic.py
+++ b/pymc/logprob/basic.py
@@ -56,7 +56,7 @@
 from pytensor.tensor.variable import TensorVariable
 
 from pymc.logprob.abstract import (
-    MeasurableVariable,
+    MeasurableOp,
     _icdf_helper,
     _logcdf_helper,
     _logprob,
@@ -522,7 +522,7 @@ def conditional_logp(
     while q:
         node = q.popleft()
 
-        if not isinstance(node.op, MeasurableVariable):
+        if not isinstance(node.op, MeasurableOp):
             continue
 
         q_values = [replacements[q_rv] for q_rv in node.outputs if q_rv in updated_rv_values]
diff --git a/pymc/logprob/checks.py b/pymc/logprob/checks.py
index f7b483e599..fdee9d689a 100644
--- a/pymc/logprob/checks.py
+++ b/pymc/logprob/checks.py
@@ -42,18 +42,15 @@
 from pytensor.tensor import TensorVariable
 from pytensor.tensor.shape import SpecifyShape
 
-from pymc.logprob.abstract import MeasurableVariable, _logprob, _logprob_helper
+from pymc.logprob.abstract import MeasurableOp, MeasurableOpMixin, _logprob, _logprob_helper
 from pymc.logprob.rewriting import PreserveRVMappings, measurable_ir_rewrites_db
 from pymc.logprob.utils import replace_rvs_by_values
 
 
-class MeasurableSpecifyShape(SpecifyShape):
+class MeasurableSpecifyShape(MeasurableOpMixin, SpecifyShape):
     """A placeholder used to specify a log-likelihood for a specify-shape sub-graph."""
 
 
-MeasurableVariable.register(MeasurableSpecifyShape)
-
-
 @_logprob.register(MeasurableSpecifyShape)
 def logprob_specify_shape(op, values, inner_rv, *shapes, **kwargs):
     (value,) = values
@@ -80,7 +77,7 @@ def find_measurable_specify_shapes(fgraph, node) -> list[TensorVariable] | None:
 
     if not (
         base_rv.owner
-        and isinstance(base_rv.owner.op, MeasurableVariable)
+        and isinstance(base_rv.owner.op, MeasurableOp)
         and base_rv not in rv_map_feature.rv_values
     ):
         return None  # pragma: no cover
@@ -99,13 +96,10 @@ def find_measurable_specify_shapes(fgraph, node) -> list[TensorVariable] | None:
 )
 
 
-class MeasurableCheckAndRaise(CheckAndRaise):
+class MeasurableCheckAndRaise(MeasurableOpMixin, CheckAndRaise):
     """A placeholder used to specify a log-likelihood for an assert sub-graph."""
 
 
-MeasurableVariable.register(MeasurableCheckAndRaise)
-
-
 @_logprob.register(MeasurableCheckAndRaise)
 def logprob_check_and_raise(op, values, inner_rv, *assertions, **kwargs):
     (value,) = values
diff --git a/pymc/logprob/cumsum.py b/pymc/logprob/cumsum.py
index 3c1c9d3e72..777cb05da5 100644
--- a/pymc/logprob/cumsum.py
+++ b/pymc/logprob/cumsum.py
@@ -41,17 +41,14 @@
 from pytensor.tensor import TensorVariable
 from pytensor.tensor.extra_ops import CumOp
 
-from pymc.logprob.abstract import MeasurableVariable, _logprob, _logprob_helper
+from pymc.logprob.abstract import MeasurableOpMixin, _logprob, _logprob_helper
 from pymc.logprob.rewriting import PreserveRVMappings, measurable_ir_rewrites_db
 
 
-class MeasurableCumsum(CumOp):
+class MeasurableCumsum(MeasurableOpMixin, CumOp):
     """A placeholder used to specify a log-likelihood for a cumsum sub-graph."""
 
 
-MeasurableVariable.register(MeasurableCumsum)
-
-
 @_logprob.register(MeasurableCumsum)
 def logprob_cumsum(op, values, base_rv, **kwargs):
     """Compute the log-likelihood graph for a `Cumsum`."""
diff --git a/pymc/logprob/mixture.py b/pymc/logprob/mixture.py
index 08e102f805..15e4e2a82e 100644
--- a/pymc/logprob/mixture.py
+++ b/pymc/logprob/mixture.py
@@ -67,7 +67,8 @@
 
 from pymc.logprob.abstract import (
     MeasurableElemwise,
-    MeasurableVariable,
+    MeasurableOp,
+    MeasurableOpMixin,
     _logprob,
     _logprob_helper,
 )
@@ -217,7 +218,7 @@ def rv_pull_down(x: TensorVariable) -> TensorVariable:
     return fgraph.outputs[0]
 
 
-class MixtureRV(Op):
+class MixtureRV(MeasurableOpMixin, Op):
     """A placeholder used to specify a log-likelihood for a mixture sub-graph."""
 
     __props__ = ("indices_end_idx", "out_dtype", "out_broadcastable")
@@ -235,9 +236,6 @@ def perform(self, node, inputs, outputs):
         raise NotImplementedError("This is a stand-in Op.")  # pragma: no cover
 
 
-MeasurableVariable.register(MixtureRV)
-
-
 def get_stack_mixture_vars(
     node: Apply,
 ) -> tuple[list[TensorVariable] | None, int | None]:
@@ -457,13 +455,10 @@ def logprob_switch_mixture(op, values, switch_cond, component_true, component_fa
 )
 
 
-class MeasurableIfElse(IfElse):
+class MeasurableIfElse(MeasurableOpMixin, IfElse):
     """Measurable subclass of IfElse operator."""
 
 
-MeasurableVariable.register(MeasurableIfElse)
-
-
 @node_rewriter([IfElse])
 def useless_ifelse_outputs(fgraph, node):
     """Remove outputs that are shared across the IfElse branches."""
@@ -512,7 +507,7 @@ def find_measurable_ifelse_mixture(fgraph, node):
     base_rvs = assume_measured_ir_outputs(valued_rvs, base_rvs)
     if len(base_rvs) != op.n_outs * 2:
         return None
-    if not all(var.owner and isinstance(var.owner.op, MeasurableVariable) for var in base_rvs):
+    if not all(var.owner and isinstance(var.owner.op, MeasurableOp) for var in base_rvs):
         return None
 
     return MeasurableIfElse(n_outs=op.n_outs).make_node(if_var, *base_rvs).outputs
diff --git a/pymc/logprob/order.py b/pymc/logprob/order.py
index f9fa8cbe0d..a765756be9 100644
--- a/pymc/logprob/order.py
+++ b/pymc/logprob/order.py
@@ -48,7 +48,7 @@
 from pytensor.tensor.variable import TensorVariable
 
 from pymc.logprob.abstract import (
-    MeasurableVariable,
+    MeasurableOpMixin,
     _logcdf_helper,
     _logprob,
     _logprob_helper,
@@ -59,20 +59,14 @@
 from pymc.pytensorf import constant_fold
 
 
-class MeasurableMax(Max):
+class MeasurableMax(MeasurableOpMixin, Max):
     """A placeholder used to specify a log-likelihood for a max sub-graph."""
 
 
-MeasurableVariable.register(MeasurableMax)
-
-
-class MeasurableMaxDiscrete(Max):
+class MeasurableMaxDiscrete(MeasurableOpMixin, Max):
     """A placeholder used to specify a log-likelihood for sub-graphs of maxima of discrete variables"""
 
 
-MeasurableVariable.register(MeasurableMaxDiscrete)
-
-
 @node_rewriter([Max])
 def find_measurable_max(fgraph: FunctionGraph, node: Apply) -> list[TensorVariable] | None:
     rv_map_feature = getattr(fgraph, "preserve_rv_mappings", None)
@@ -162,21 +156,15 @@ def max_logprob_discrete(op, values, base_rv, **kwargs):
     return logprob
 
 
-class MeasurableMaxNeg(Max):
+class MeasurableMaxNeg(MeasurableOpMixin, Max):
     """A placeholder used to specify a log-likelihood for a max(neg(x)) sub-graph.
     This shows up in the graph of min, which is (neg(max(neg(x)))."""
 
 
-MeasurableVariable.register(MeasurableMaxNeg)
-
-
-class MeasurableDiscreteMaxNeg(Max):
+class MeasurableDiscreteMaxNeg(MeasurableOpMixin, Max):
     """A placeholder used to specify a log-likelihood for sub-graphs of negative maxima of discrete variables"""
 
 
-MeasurableVariable.register(MeasurableDiscreteMaxNeg)
-
-
 @node_rewriter(tracks=[Max])
 def find_measurable_max_neg(fgraph: FunctionGraph, node: Apply) -> list[TensorVariable] | None:
     rv_map_feature = getattr(fgraph, "preserve_rv_mappings", None)
diff --git a/pymc/logprob/rewriting.py b/pymc/logprob/rewriting.py
index a7dca970d5..aa3586c21e 100644
--- a/pymc/logprob/rewriting.py
+++ b/pymc/logprob/rewriting.py
@@ -81,7 +81,7 @@
 )
 from pytensor.tensor.variable import TensorVariable
 
-from pymc.logprob.abstract import MeasurableVariable
+from pymc.logprob.abstract import MeasurableOp
 from pymc.logprob.utils import DiracDelta
 
 inc_subtensor_ops = (IncSubtensor, AdvancedIncSubtensor, AdvancedIncSubtensor1)
@@ -139,7 +139,7 @@ def apply(self, fgraph):
                     continue
                 # This is where we filter only those nodes we care about:
                 # Nodes that have variables that we want to measure and are not yet measurable
-                if isinstance(node.op, MeasurableVariable):
+                if isinstance(node.op, MeasurableOp):
                     continue
                 if not any(out in rv_map_feature.needs_measuring for out in node.outputs):
                     continue
@@ -155,7 +155,7 @@ def apply(self, fgraph):
                             node_rewriter, "__name__", ""
                         )
                     # If we converted to a MeasurableVariable we're done here!
-                    if node not in fgraph.apply_nodes or isinstance(node.op, MeasurableVariable):
+                    if node not in fgraph.apply_nodes or isinstance(node.op, MeasurableOp):
                         # go to next node
                         break
 
@@ -274,7 +274,7 @@ def request_measurable(self, vars: Sequence[Variable]) -> list[Variable]:
             # Input vars or valued vars can't be measured for derived expressions
             if not var.owner or var in self.rv_values:
                 continue
-            if isinstance(var.owner.op, MeasurableVariable):
+            if isinstance(var.owner.op, MeasurableOp):
                 measurable.append(var)
             else:
                 self.needs_measuring.add(var)
diff --git a/pymc/logprob/scan.py b/pymc/logprob/scan.py
index 84b2722b1a..2adba3297c 100644
--- a/pymc/logprob/scan.py
+++ b/pymc/logprob/scan.py
@@ -54,7 +54,7 @@
 from pytensor.tensor.variable import TensorVariable
 from pytensor.updates import OrderedUpdates
 
-from pymc.logprob.abstract import MeasurableVariable, _logprob
+from pymc.logprob.abstract import MeasurableOp, MeasurableOpMixin, _logprob
 from pymc.logprob.basic import conditional_logp
 from pymc.logprob.rewriting import (
     PreserveRVMappings,
@@ -66,16 +66,13 @@
 from pymc.logprob.utils import replace_rvs_by_values
 
 
-class MeasurableScan(Scan):
+class MeasurableScan(MeasurableOpMixin, Scan):
     """A placeholder used to specify a log-likelihood for a scan sub-graph."""
 
     def __str__(self):
         return f"Measurable({super().__str__()})"
 
 
-MeasurableVariable.register(MeasurableScan)
-
-
 def convert_outer_out_to_in(
     input_scan_args: ScanArgs,
     outer_out_vars: Iterable[TensorVariable],
@@ -288,7 +285,7 @@ def get_random_outer_outputs(
         io_type = oo_info.name[(oo_info.name.index("_", 6) + 1) :]
         inner_out_type = f"inner_out_{io_type}"
         io_var = getattr(scan_args, inner_out_type)[oo_info.index]
-        if io_var.owner and isinstance(io_var.owner.op, MeasurableVariable):
+        if io_var.owner and isinstance(io_var.owner.op, MeasurableOp):
             rv_vars.append((n, oo_var, io_var))
     return rv_vars
 
diff --git a/pymc/logprob/tensor.py b/pymc/logprob/tensor.py
index c709013cc6..d6d946cdf4 100644
--- a/pymc/logprob/tensor.py
+++ b/pymc/logprob/tensor.py
@@ -52,7 +52,7 @@
     local_rv_size_lift,
 )
 
-from pymc.logprob.abstract import MeasurableVariable, _logprob, _logprob_helper
+from pymc.logprob.abstract import MeasurableOp, MeasurableOpMixin, _logprob, _logprob_helper
 from pymc.logprob.rewriting import (
     PreserveRVMappings,
     assume_measured_ir_outputs,
@@ -124,13 +124,10 @@ def naive_bcast_rv_lift(fgraph: FunctionGraph, node):
     return [bcasted_node.outputs[1]]
 
 
-class MeasurableMakeVector(MakeVector):
+class MeasurableMakeVector(MeasurableOpMixin, MakeVector):
     """A placeholder used to specify a log-likelihood for a cumsum sub-graph."""
 
 
-MeasurableVariable.register(MeasurableMakeVector)
-
-
 @_logprob.register(MeasurableMakeVector)
 def logprob_make_vector(op, values, *base_rvs, **kwargs):
     """Compute the log-likelihood graph for a `MeasurableMakeVector`."""
@@ -151,13 +148,10 @@ def logprob_make_vector(op, values, *base_rvs, **kwargs):
     return pt.stack(logps)
 
 
-class MeasurableJoin(Join):
+class MeasurableJoin(MeasurableOpMixin, Join):
     """A placeholder used to specify a log-likelihood for a join sub-graph."""
 
 
-MeasurableVariable.register(MeasurableJoin)
-
-
 @_logprob.register(MeasurableJoin)
 def logprob_join(op, values, axis, *base_rvs, **kwargs):
     """Compute the log-likelihood graph for a `Join`."""
@@ -222,7 +216,7 @@ def find_measurable_stacks(fgraph, node) -> list[TensorVariable] | None:
         return None
 
     base_vars = assume_measured_ir_outputs(valued_rvs, base_vars)
-    if not all(var.owner and isinstance(var.owner.op, MeasurableVariable) for var in base_vars):
+    if not all(var.owner and isinstance(var.owner.op, MeasurableOp) for var in base_vars):
         return None
 
     if is_join:
@@ -234,7 +228,7 @@ def find_measurable_stacks(fgraph, node) -> list[TensorVariable] | None:
     return [measurable_stack]
 
 
-class MeasurableDimShuffle(DimShuffle):
+class MeasurableDimShuffle(MeasurableOpMixin, DimShuffle):
     """A placeholder used to specify a log-likelihood for a dimshuffle sub-graph."""
 
     # Need to get the absolute path of `c_func_file`, otherwise it tries to
@@ -242,9 +236,6 @@ class MeasurableDimShuffle(DimShuffle):
     c_func_file = str(DimShuffle.get_path(Path(DimShuffle.c_func_file)))
 
 
-MeasurableVariable.register(MeasurableDimShuffle)
-
-
 @_logprob.register(MeasurableDimShuffle)
 def logprob_dimshuffle(op: MeasurableDimShuffle, values, base_var, **kwargs):
     """Compute the log-likelihood graph for a `MeasurableDimShuffle`."""
diff --git a/pymc/logprob/transform_value.py b/pymc/logprob/transform_value.py
index 00422e6529..2523a9b6db 100644
--- a/pymc/logprob/transform_value.py
+++ b/pymc/logprob/transform_value.py
@@ -24,7 +24,7 @@
 from pytensor.scan.op import Scan
 from pytensor.tensor.variable import TensorVariable
 
-from pymc.logprob.abstract import MeasurableVariable, _logprob
+from pymc.logprob.abstract import MeasurableOp, _logprob
 from pymc.logprob.rewriting import PreserveRVMappings, cleanup_ir_rewrites_db
 from pymc.logprob.transforms import Transform
 
@@ -50,7 +50,7 @@ def infer_shape(self, fgraph, node, input_shapes):
 transformed_value = TransformedValue()
 
 
-class TransformedValueRV(Op):
+class TransformedValueRV(MeasurableOp, Op):
     """A no-op that identifies RVs whose values were transformed.
 
     This is introduced by the `TransformValuesRewrite`
@@ -76,9 +76,6 @@ def infer_shape(self, fgraph, node, input_shapes):
         return input_shapes
 
 
-MeasurableVariable.register(TransformedValueRV)
-
-
 @_logprob.register(TransformedValueRV)
 def transformed_value_logprob(op, values, *rv_outs, use_jacobian=True, **kwargs):
     """Compute the log-probability graph for a `TransformedRV`.
diff --git a/pymc/logprob/transforms.py b/pymc/logprob/transforms.py
index 505b51cb7e..b2a9717f02 100644
--- a/pymc/logprob/transforms.py
+++ b/pymc/logprob/transforms.py
@@ -108,7 +108,7 @@
 
 from pymc.logprob.abstract import (
     MeasurableElemwise,
-    MeasurableVariable,
+    MeasurableOp,
     _icdf,
     _icdf_helper,
     _logcdf,
@@ -427,7 +427,7 @@ def find_measurable_transforms(fgraph: FunctionGraph, node: Node) -> list[Node]
     """Find measurable transformations from Elemwise operators."""
 
     # Node was already converted
-    if isinstance(node.op, MeasurableVariable):
+    if isinstance(node.op, MeasurableOp):
         return None  # pragma: no cover
 
     rv_map_feature: PreserveRVMappings | None = getattr(fgraph, "preserve_rv_mappings", None)
diff --git a/pymc/logprob/utils.py b/pymc/logprob/utils.py
index b75e633f73..e5e878f0b5 100644
--- a/pymc/logprob/utils.py
+++ b/pymc/logprob/utils.py
@@ -55,7 +55,7 @@
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.variable import TensorVariable
 
-from pymc.logprob.abstract import MeasurableVariable, _logprob
+from pymc.logprob.abstract import MeasurableOp, _logprob
 from pymc.pytensorf import replace_vars_in_graphs
 from pymc.util import makeiter
 
@@ -147,7 +147,7 @@ def expand(r):
     return {
         node
         for node in walk(makeiter(vars), expand, False)
-        if node.owner and isinstance(node.owner.op, RandomVariable | MeasurableVariable)
+        if node.owner and isinstance(node.owner.op, RandomVariable | MeasurableOp)
     }
 
 
@@ -179,7 +179,7 @@ def check_potential_measurability(
 
     def expand_fn(var):
         # expand_fn does not go beyond valued_rvs or any MeasurableVariable
-        if var.owner and not isinstance(var.owner.op, MeasurableVariable) and var not in valued_rvs:
+        if var.owner and not isinstance(var.owner.op, MeasurableOp) and var not in valued_rvs:
             return reversed(var.owner.inputs)
         else:
             return []
@@ -189,7 +189,7 @@ def expand_fn(var):
         for ancestor_var in walk(inputs, expand=expand_fn, bfs=False)
         if (
             ancestor_var.owner
-            and isinstance(ancestor_var.owner.op, MeasurableVariable)
+            and isinstance(ancestor_var.owner.op, MeasurableOp)
             and ancestor_var not in valued_rvs
         )
     ):
@@ -259,7 +259,7 @@ def local_check_parameter_to_ninf_switch(fgraph, node):
 )
 
 
-class DiracDelta(Op):
+class DiracDelta(MeasurableOp, Op):
     """An `Op` that represents a Dirac-delta distribution."""
 
     __props__ = ("rtol", "atol")
@@ -287,9 +287,6 @@ def infer_shape(self, fgraph, node, input_shapes):
         return input_shapes
 
 
-MeasurableVariable.register(DiracDelta)
-
-
 dirac_delta = DiracDelta()
 
 
diff --git a/pymc/variational/minibatch_rv.py b/pymc/variational/minibatch_rv.py
index 435cac9fbb..864825910d 100644
--- a/pymc/variational/minibatch_rv.py
+++ b/pymc/variational/minibatch_rv.py
@@ -20,10 +20,10 @@
 from pytensor.graph import Apply, Op
 from pytensor.tensor import NoneConst, TensorVariable, as_tensor_variable
 
-from pymc.logprob.abstract import MeasurableVariable, _logprob, _logprob_helper
+from pymc.logprob.abstract import MeasurableOp, _logprob, _logprob_helper
 
 
-class MinibatchRandomVariable(Op):
+class MinibatchRandomVariable(MeasurableOp, Op):
     """RV whose logprob should be rescaled to match total_size"""
 
     __props__ = ()
@@ -95,9 +95,6 @@ def get_scaling(total_size: Sequence[Variable], shape: TensorVariable) -> Tensor
     return pt.cast(coef, dtype=config.floatX)
 
 
-MeasurableVariable.register(MinibatchRandomVariable)
-
-
 @_logprob.register(MinibatchRandomVariable)
 def minibatch_rv_logprob(op, values, *inputs, **kwargs):
     [value] = values
diff --git a/tests/logprob/test_abstract.py b/tests/logprob/test_abstract.py
index 7a0bc61e78..3976066e60 100644
--- a/tests/logprob/test_abstract.py
+++ b/tests/logprob/test_abstract.py
@@ -45,7 +45,7 @@
 
 import pymc as pm
 
-from pymc.logprob.abstract import MeasurableElemwise, MeasurableVariable, _logcdf_helper
+from pymc.logprob.abstract import MeasurableElemwise, MeasurableOp, _logcdf_helper
 from pymc.logprob.basic import logcdf
 
 
@@ -66,7 +66,7 @@ class TestMeasurableElemwise(MeasurableElemwise):
 
     measurable_exp_op = TestMeasurableElemwise(scalar_op=exp)
     measurable_exp = measurable_exp_op(0.0)
-    assert isinstance(measurable_exp.owner.op, MeasurableVariable)
+    assert isinstance(measurable_exp.owner.op, MeasurableOp)
 
 
 def test_logcdf_helper():
diff --git a/tests/logprob/test_composite_logprob.py b/tests/logprob/test_composite_logprob.py
index e4cdfc7dc3..3653830ef9 100644
--- a/tests/logprob/test_composite_logprob.py
+++ b/tests/logprob/test_composite_logprob.py
@@ -41,7 +41,7 @@
 import scipy.stats as st
 
 from pymc import draw, logp
-from pymc.logprob.abstract import MeasurableVariable
+from pymc.logprob.abstract import MeasurableOp
 from pymc.logprob.basic import conditional_logp
 from pymc.logprob.rewriting import construct_ir_fgraph
 from pymc.testing import assert_no_rvs
@@ -138,7 +138,7 @@ def test_unvalued_ir_reversion(nested):
 
     # assert len(z_fgraph.preserve_rv_mappings.measurable_conversions) == 1
     assert (
-        sum(isinstance(node.op, MeasurableVariable) for node in z_fgraph.apply_nodes) == 2
+        sum(isinstance(node.op, MeasurableOp) for node in z_fgraph.apply_nodes) == 2
     )  # Just the 2 rvs
 
 
diff --git a/tests/logprob/test_mixture.py b/tests/logprob/test_mixture.py
index fa0c53831e..1d09e844fd 100644
--- a/tests/logprob/test_mixture.py
+++ b/tests/logprob/test_mixture.py
@@ -52,7 +52,7 @@
     as_index_constant,
 )
 
-from pymc.logprob.abstract import MeasurableVariable
+from pymc.logprob.abstract import MeasurableOp
 from pymc.logprob.basic import conditional_logp, logp
 from pymc.logprob.mixture import MeasurableSwitchMixture, expand_indices
 from pymc.logprob.rewriting import construct_ir_fgraph
@@ -993,16 +993,16 @@ def test_switch_mixture_invalid_bcast():
 
     valid_mix = pt.switch(valid_switch_cond, valid_true_branch, valid_false_branch)
     fgraph, _, _ = construct_ir_fgraph({valid_mix: valid_mix.type()})
-    assert isinstance(fgraph.outputs[0].owner.op, MeasurableVariable)
+    assert isinstance(fgraph.outputs[0].owner.op, MeasurableOp)
     assert isinstance(fgraph.outputs[0].owner.op, MeasurableSwitchMixture)
 
     invalid_mix = pt.switch(invalid_switch_cond, valid_true_branch, valid_false_branch)
     fgraph, _, _ = construct_ir_fgraph({invalid_mix: invalid_mix.type()})
-    assert not isinstance(fgraph.outputs[0].owner.op, MeasurableVariable)
+    assert not isinstance(fgraph.outputs[0].owner.op, MeasurableOp)
 
     invalid_mix = pt.switch(valid_switch_cond, valid_true_branch, invalid_false_branch)
     fgraph, _, _ = construct_ir_fgraph({invalid_mix: invalid_mix.type()})
-    assert not isinstance(fgraph.outputs[0].owner.op, MeasurableVariable)
+    assert not isinstance(fgraph.outputs[0].owner.op, MeasurableOp)
 
 
 def test_ifelse_mixture_one_component():
diff --git a/tests/logprob/test_transform_value.py b/tests/logprob/test_transform_value.py
index 2490ab61e7..3832b44143 100644
--- a/tests/logprob/test_transform_value.py
+++ b/tests/logprob/test_transform_value.py
@@ -30,7 +30,7 @@
 
 from pymc.distributions.transforms import _default_transform, log, logodds
 from pymc.logprob import conditional_logp
-from pymc.logprob.abstract import MeasurableVariable, _logprob
+from pymc.logprob.abstract import MeasurableOp, _logprob
 from pymc.logprob.transform_value import TransformValuesMapping, TransformValuesRewrite
 from pymc.logprob.transforms import ExpTransform, LogOddsTransform, LogTransform
 from pymc.testing import assert_no_rvs
@@ -42,14 +42,12 @@ def multiout_measurable_op():
     # Create a dummy Op that just returns the two inputs
     mu1, mu2 = pt.scalars("mu1", "mu2")
 
-    class TestOpFromGraph(OpFromGraph):
+    class TestOpFromGraph(MeasurableOp, OpFromGraph):
         def do_constant_folding(self, fgraph, node):
             False
 
     multiout_op = TestOpFromGraph([mu1, mu2], [mu1 + 0.0, mu2 + 0.0])
 
-    MeasurableVariable.register(TestOpFromGraph)
-
     @_logprob.register(TestOpFromGraph)
     def logp_multiout(op, values, mu1, mu2):
         value1, value2 = values
diff --git a/tests/logprob/test_utils.py b/tests/logprob/test_utils.py
index 3192d0c586..d337e0317e 100644
--- a/tests/logprob/test_utils.py
+++ b/tests/logprob/test_utils.py
@@ -49,7 +49,7 @@
 
 from pymc import SymbolicRandomVariable, inputvars
 from pymc.distributions.transforms import Interval
-from pymc.logprob.abstract import MeasurableVariable
+from pymc.logprob.abstract import MeasurableOp
 from pymc.logprob.basic import logp
 from pymc.logprob.utils import (
     ParameterValueError,
@@ -151,13 +151,7 @@ def test_intermediate_rv(self):
         res_ancestors = list(ancestors((res,)))
 
         assert (
-            len(
-                list(
-                    n
-                    for n in res_ancestors
-                    if n.owner and isinstance(n.owner.op, MeasurableVariable)
-                )
-            )
+            len(list(n for n in res_ancestors if n.owner and isinstance(n.owner.op, MeasurableOp)))
             == 1
         )