diff --git a/pymc/data.py b/pymc/data.py
index 4b3538a34..507f547e5 100644
--- a/pymc/data.py
+++ b/pymc/data.py
@@ -14,7 +14,6 @@
 
 import io
 import urllib.request
-import warnings
 
 from collections.abc import Sequence
 from copy import copy
@@ -40,10 +39,8 @@
 from pymc.vartypes import isgenerator
 
 __all__ = [
-    "ConstantData",
     "Data",
     "Minibatch",
-    "MutableData",
     "get_data",
 ]
 BASE_URL = "https://raw.githubusercontent.com/pymc-devs/pymc-examples/main/examples/data/{filename}"
@@ -218,66 +215,6 @@ def determine_coords(
     return coords, new_dims
 
 
-def ConstantData(
-    name: str,
-    value,
-    *,
-    dims: Sequence[str] | None = None,
-    coords: dict[str, Sequence | np.ndarray] | None = None,
-    infer_dims_and_coords=False,
-    **kwargs,
-) -> TensorConstant:
-    """Alias for ``pm.Data``.
-
-    Registers the ``value`` as a :class:`~pytensor.tensor.TensorConstant` with the model.
-    For more information, please reference :class:`pymc.Data`.
-    """
-    warnings.warn(
-        "ConstantData is deprecated. All Data variables are now mutable. Use Data instead.",
-        FutureWarning,
-    )
-
-    var = Data(
-        name,
-        value,
-        dims=dims,
-        coords=coords,
-        infer_dims_and_coords=infer_dims_and_coords,
-        **kwargs,
-    )
-    return cast(TensorConstant, var)
-
-
-def MutableData(
-    name: str,
-    value,
-    *,
-    dims: Sequence[str] | None = None,
-    coords: dict[str, Sequence | np.ndarray] | None = None,
-    infer_dims_and_coords=False,
-    **kwargs,
-) -> SharedVariable:
-    """Alias for ``pm.Data``.
-
-    Registers the ``value`` as a :class:`~pytensor.compile.sharedvalue.SharedVariable`
-    with the model. For more information, please reference :class:`pymc.Data`.
-    """
-    warnings.warn(
-        "MutableData is deprecated. All Data variables are now mutable. Use Data instead.",
-        FutureWarning,
-    )
-
-    var = Data(
-        name,
-        value,
-        dims=dims,
-        coords=coords,
-        infer_dims_and_coords=infer_dims_and_coords,
-        **kwargs,
-    )
-    return cast(SharedVariable, var)
-
-
 def Data(
     name: str,
     value,
@@ -285,7 +222,6 @@ def Data(
     dims: Sequence[str] | None = None,
     coords: dict[str, Sequence | np.ndarray] | None = None,
     infer_dims_and_coords=False,
-    mutable: bool | None = None,
     **kwargs,
 ) -> SharedVariable | TensorConstant:
     """Create a data container that registers a data variable with the model.
@@ -380,11 +316,6 @@ def Data(
             "Pass them directly to `observed` if you want to trigger auto-imputation"
         )
 
-    if mutable is not None:
-        warnings.warn(
-            "Data is now always mutable. Specifying the `mutable` kwarg will raise an error in a future release",
-            FutureWarning,
-        )
     x = pytensor.shared(arr, name, **kwargs)
 
     if isinstance(dims, str):
diff --git a/pymc/distributions/custom.py b/pymc/distributions/custom.py
index 86aba1204..476d5bc41 100644
--- a/pymc/distributions/custom.py
+++ b/pymc/distributions/custom.py
@@ -13,7 +13,6 @@
 #   limitations under the License.
 import functools
 import re
-import warnings
 
 from collections.abc import Callable, Sequence
 
@@ -715,13 +714,6 @@ def __new__(
             )
         dist_params = cls.parse_dist_params(dist_params)
         cls.check_valid_dist_random(dist, random, dist_params)
-        moment = kwargs.pop("moment", None)
-        if moment is not None:
-            warnings.warn(
-                "`moment` argument is deprecated. Use `support_point` instead.",
-                FutureWarning,
-            )
-            support_point = moment
         if dist is not None:
             kwargs.setdefault("class_name", f"CustomDist_{name}")
             return _CustomSymbolicDist(
diff --git a/pymc/distributions/dist_math.py b/pymc/distributions/dist_math.py
index 3f675406f..55efcb3b3 100644
--- a/pymc/distributions/dist_math.py
+++ b/pymc/distributions/dist_math.py
@@ -18,8 +18,6 @@
 @author: johnsalvatier
 """
 
-import warnings
-
 from collections.abc import Iterable
 from functools import partial
 
@@ -419,12 +417,3 @@ def log_i0(x):
             + 11025.0 / (98304.0 * x**4.0)
         ),
     )
-
-
-def incomplete_beta(a, b, value):
-    warnings.warn(
-        "incomplete_beta has been deprecated. Use pytensor.tensor.betainc instead.",
-        FutureWarning,
-        stacklevel=2,
-    )
-    return pt.betainc(a, b, value)
diff --git a/pymc/distributions/distribution.py b/pymc/distributions/distribution.py
index 3f080b7e5..f8712c51e 100644
--- a/pymc/distributions/distribution.py
+++ b/pymc/distributions/distribution.py
@@ -60,7 +60,7 @@
     convert_observed_data,
     floatX,
 )
-from pymc.util import UNSET, _add_future_warning_tag
+from pymc.util import UNSET
 from pymc.vartypes import continuous_types, string_types
 
 __all__ = [
@@ -571,10 +571,7 @@ def dist(
             ndim_supp = cls.rv_op(*dist_params, **kwargs).owner.op.ndim_supp
 
         create_size = find_size(shape=shape, size=size, ndim_supp=ndim_supp)
-        rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)
-
-        _add_future_warning_tag(rv_out)
-        return rv_out
+        return cls.rv_op(*dist_params, size=create_size, **kwargs)
 
 
 @node_rewriter([SymbolicRandomVariable])
diff --git a/pymc/distributions/shape_utils.py b/pymc/distributions/shape_utils.py
index 6f54aba2d..8ef378b76 100644
--- a/pymc/distributions/shape_utils.py
+++ b/pymc/distributions/shape_utils.py
@@ -43,7 +43,6 @@
 
 from pymc.exceptions import ShapeError
 from pymc.pytensorf import PotentialShapeType
-from pymc.util import _add_future_warning_tag
 
 
 def to_tuple(shape):
@@ -264,7 +263,6 @@ def change_dist_size(
 
     op = dist.owner.op
     new_dist = _change_dist_size(op, dist, new_size=new_size, expand=expand)
-    _add_future_warning_tag(new_dist)
 
     new_dist.name = dist.name
     for k, v in dist.tag.__dict__.items():
diff --git a/pymc/distributions/timeseries.py b/pymc/distributions/timeseries.py
index 87347add1..f18e1f4c2 100644
--- a/pymc/distributions/timeseries.py
+++ b/pymc/distributions/timeseries.py
@@ -286,13 +286,6 @@ class GaussianRandomWalk(PredefinedRandomWalk):
 
     @classmethod
     def get_dists(cls, mu=0.0, sigma=1.0, *, init_dist=None, **kwargs):
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         if init_dist is None:
             warnings.warn(
                 "Initial distribution not specified, defaulting to `Normal.dist(0, 100)`."
@@ -340,14 +333,6 @@ class MvGaussianRandomWalk(PredefinedRandomWalk):
 
     @classmethod
     def get_dists(cls, mu, *, cov=None, tau=None, chol=None, lower=True, init_dist=None, **kwargs):
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error "
-                "in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         if init_dist is None:
             warnings.warn(
                 "Initial distribution not specified, defaulting to `MvNormal.dist(0, I*100)`."
@@ -396,14 +381,6 @@ class MvStudentTRandomWalk(PredefinedRandomWalk):
     def get_dists(
         cls, *, nu, mu, scale=None, tau=None, chol=None, lower=True, init_dist=None, **kwargs
     ):
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error "
-                "in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         if init_dist is None:
             warnings.warn(
                 "Initial distribution not specified, defaulting to `MvNormal.dist(0, I*100)`."
@@ -588,13 +565,6 @@ def dist(
         sigma = pt.as_tensor_variable(sigma)
         rhos = pt.atleast_1d(pt.as_tensor_variable(rho))
 
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         ar_order = cls._get_ar_order(rhos=rhos, constant=constant, ar_order=ar_order)
         steps = get_support_shape_1d(
             support_shape=steps, shape=kwargs.get("shape", None), support_shape_offset=ar_order
diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index ebdaf3c3e..b1e02fd1f 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -11,7 +11,6 @@
 #   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.
-import warnings
 
 from functools import singledispatch
 
@@ -48,22 +47,6 @@
 ]
 
 
-def __getattr__(name):
-    if name in ("univariate_ordered", "multivariate_ordered"):
-        warnings.warn(f"{name} has been deprecated, use ordered instead.", FutureWarning)
-        return ordered
-
-    if name in ("univariate_sum_to_1", "multivariate_sum_to_1"):
-        warnings.warn(f"{name} has been deprecated, use sum_to_1 instead.", FutureWarning)
-        return sum_to_1
-
-    if name == "RVTransform":
-        warnings.warn("RVTransform has been renamed to Transform", FutureWarning)
-        return Transform
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")
-
-
 @singledispatch
 def _default_transform(op: Op, rv: TensorVariable):
     """Return default transform for a given Distribution `Op`."""
@@ -100,9 +83,7 @@ class Ordered(Transform):
 
     name = "ordered"
 
-    def __init__(self, ndim_supp=None, positive=False, ascending=True):
-        if ndim_supp is not None:
-            warnings.warn("ndim_supp argument is deprecated and has no effect", FutureWarning)
+    def __init__(self, positive=False, ascending=True):
         self.positive = positive
         self.ascending = ascending
 
@@ -142,10 +123,6 @@ class SumTo1(Transform):
 
     name = "sumto1"
 
-    def __init__(self, ndim_supp=None):
-        if ndim_supp is not None:
-            warnings.warn("ndim_supp argument is deprecated and has no effect", FutureWarning)
-
     def backward(self, value, *inputs):
         remaining = 1 - pt.sum(value[..., :], axis=-1, keepdims=True)
         return pt.concatenate([value[..., :], remaining], axis=-1)
diff --git a/pymc/initial_point.py b/pymc/initial_point.py
index c276a5c49..df9419c74 100644
--- a/pymc/initial_point.py
+++ b/pymc/initial_point.py
@@ -240,12 +240,6 @@ def make_initial_point_expression(
             strategy = default_strategy
 
         if isinstance(strategy, str):
-            if strategy == "moment":
-                strategy = "support_point"
-                warnings.warn(
-                    "The 'moment' strategy is deprecated. Use 'support_point' instead.",
-                    FutureWarning,
-                )
             if strategy == "support_point":
                 try:
                     value = support_point(variable)
diff --git a/pymc/logprob/abstract.py b/pymc/logprob/abstract.py
index 5c7f28e66..564dfa1fa 100644
--- a/pymc/logprob/abstract.py
+++ b/pymc/logprob/abstract.py
@@ -35,7 +35,6 @@
 #   SOFTWARE.
 
 import abc
-import warnings
 
 from collections.abc import Sequence
 from functools import singledispatch
@@ -48,17 +47,6 @@
 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 MeasurableOp
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")
-
-
 @singledispatch
 def _logprob(
     op: Op,
diff --git a/pymc/logprob/basic.py b/pymc/logprob/basic.py
index 6fd4a5489..85d74aab3 100644
--- a/pymc/logprob/basic.py
+++ b/pymc/logprob/basic.py
@@ -98,23 +98,7 @@ def _warn_rvs_in_inferred_graph(graph: TensorVariable | Sequence[TensorVariable]
         )
 
 
-def _deprecate_warn_missing_rvs(warn_rvs, kwargs):
-    if "warn_missing_rvs" in kwargs:
-        warnings.warn(
-            "Argument `warn_missing_rvs` was renamed to `warn_rvs` and will be removed in a future release",
-            FutureWarning,
-        )
-        if warn_rvs is None:
-            warn_rvs = kwargs.pop("warn_missing_rvs")
-        else:
-            raise ValueError("Can't set both warn_rvs and warn_missing_rvs")
-    else:
-        if warn_rvs is None:
-            warn_rvs = True
-    return warn_rvs, kwargs
-
-
-def logp(rv: TensorVariable, value: TensorLike, warn_rvs=None, **kwargs) -> TensorVariable:
+def logp(rv: TensorVariable, value: TensorLike, warn_rvs=True, **kwargs) -> TensorVariable:
     """Create a graph for the log-probability of a random variable.
 
     Parameters
@@ -200,8 +184,6 @@ def normal_logp(value, mu, sigma):
             pm.CustomDist("x", mu, sigma, logp=normal_logp)
 
     """
-    warn_rvs, kwargs = _deprecate_warn_missing_rvs(warn_rvs, kwargs)
-
     value = pt.as_tensor_variable(value, dtype=rv.dtype)
     try:
         return _logprob_helper(rv, value, **kwargs)
@@ -216,7 +198,7 @@ def normal_logp(value, mu, sigma):
         return expr
 
 
-def logcdf(rv: TensorVariable, value: TensorLike, warn_rvs=None, **kwargs) -> TensorVariable:
+def logcdf(rv: TensorVariable, value: TensorLike, warn_rvs=True, **kwargs) -> TensorVariable:
     """Create a graph for the log-CDF of a random variable.
 
     Parameters
@@ -301,7 +283,6 @@ def normal_logcdf(value, mu, sigma):
             pm.CustomDist("x", mu, sigma, logcdf=normal_logcdf)
 
     """
-    warn_rvs, kwargs = _deprecate_warn_missing_rvs(warn_rvs, kwargs)
     value = pt.as_tensor_variable(value, dtype=rv.dtype)
     try:
         return _logcdf_helper(rv, value, **kwargs)
@@ -317,7 +298,7 @@ def normal_logcdf(value, mu, sigma):
         return expr
 
 
-def icdf(rv: TensorVariable, value: TensorLike, warn_rvs=None, **kwargs) -> TensorVariable:
+def icdf(rv: TensorVariable, value: TensorLike, warn_rvs=True, **kwargs) -> TensorVariable:
     """Create a graph for the inverse CDF of a random variable.
 
     Parameters
@@ -384,7 +365,6 @@ def icdf(rv: TensorVariable, value: TensorLike, warn_rvs=None, **kwargs) -> Tens
         print(exp_rv_icdf_fn(value=0.9, mu=0.0))  # 3.60222448
 
     """
-    warn_rvs, kwargs = _deprecate_warn_missing_rvs(warn_rvs, kwargs)
     value = pt.as_tensor_variable(value, dtype="floatX")
     try:
         return _icdf_helper(rv, value, **kwargs)
@@ -409,7 +389,7 @@ def icdf(rv: TensorVariable, value: TensorLike, warn_rvs=None, **kwargs) -> Tens
 
 def conditional_logp(
     rv_values: dict[TensorVariable, TensorVariable],
-    warn_rvs=None,
+    warn_rvs=True,
     ir_rewriter: GraphRewriter | None = None,
     extra_rewrites: GraphRewriter | NodeRewriter | None = None,
     **kwargs,
@@ -474,8 +454,6 @@ def conditional_logp(
         from the respective `RandomVariable`.
 
     """
-    warn_rvs, kwargs = _deprecate_warn_missing_rvs(warn_rvs, kwargs)
-
     fgraph = construct_ir_fgraph(rv_values, ir_rewriter=ir_rewriter)
 
     if extra_rewrites is not None:
@@ -614,21 +592,3 @@ def transformed_conditional_logp(
         raise ValueError(RVS_IN_JOINT_LOGP_GRAPH_MSG % rvs_in_logp_expressions)
 
     return logp_terms_list
-
-
-def factorized_joint_logprob(*args, **kwargs):
-    warnings.warn(
-        "`factorized_joint_logprob` was renamed to `conditional_logp`. "
-        "The function will be removed in a future release",
-        FutureWarning,
-    )
-    return conditional_logp(*args, **kwargs)
-
-
-def joint_logp(*args, **kwargs):
-    warnings.warn(
-        "`joint_logp` was renamed to `transformed_conditional_logp`. "
-        "The function will be removed in a future release",
-        FutureWarning,
-    )
-    return transformed_conditional_logp(*args, **kwargs)
diff --git a/pymc/math.py b/pymc/math.py
index 1845dd511..13655f534 100644
--- a/pymc/math.py
+++ b/pymc/math.py
@@ -181,6 +181,8 @@
     "zeros_like",
 ]
 
+from pymc.util import UNSET
+
 
 def kronecker(*Ks):
     r"""Return the Kronecker product of arguments.
@@ -279,16 +281,6 @@ def logdiffexp(a, b):
     return a + pt.log1mexp(b - a)
 
 
-def logdiffexp_numpy(a, b):
-    """Return log(exp(a) - exp(b))."""
-    warnings.warn(
-        "pymc.math.logdiffexp_numpy is being deprecated.",
-        FutureWarning,
-        stacklevel=2,
-    )
-    return a + log1mexp_numpy(b - a, negative_input=True)
-
-
 invlogit = sigmoid
 
 
@@ -302,7 +294,7 @@ def logit(p):
     return pt.log(p / (floatX(1) - p))
 
 
-def log1mexp(x, *, negative_input=False):
+def log1mexp(x, *, negative_input=UNSET):
     r"""Return log(1 - exp(-x)).
 
     This function is numerically more stable than the naive approach.
@@ -316,50 +308,20 @@ def log1mexp(x, *, negative_input=False):
        "Accurately computing `\log(1-\exp(- \mid a \mid))` Assessed by the Rmpfr package"
 
     """
-    if not negative_input:
-        warnings.warn(
-            "pymc.math.log1mexp will expect a negative input in a future "
-            "version of PyMC.\n To suppress this warning set `negative_input=True`",
-            FutureWarning,
-            stacklevel=2,
-        )
-        x = -x
+    if negative_input is not UNSET:
+        if not negative_input:
+            raise ValueError(
+                "log1mexp with negative_input=False is no longer supported. Negate the input yourself before calling the function."
+            )
+        else:
+            warnings.warn(
+                "log1mexp with negative_input=True is now the default behavior. Specifying will fail in a future release of PyMC. Simply omit it",
+                FutureWarning,
+            )
 
     return pt.log1mexp(x)
 
 
-def log1mexp_numpy(x, *, negative_input=False):
-    """Return log(1 - exp(x)).
-
-    This function is numerically more stable than the naive approach.
-
-    For details, see
-    https://cran.r-project.org/web/packages/Rmpfr/vignettes/log1mexp-note.pdf
-    """
-    warnings.warn(
-        "pymc.math.log1mexp_numpy is being deprecated.",
-        FutureWarning,
-        stacklevel=2,
-    )
-    x = np.asarray(x, dtype="float")
-
-    if not negative_input:
-        warnings.warn(
-            "pymc.math.log1mexp_numpy will expect a negative input in a future "
-            "version of PyMC.\n To suppress this warning set `negative_input=True`",
-            FutureWarning,
-            stacklevel=2,
-        )
-        x = -x
-
-    out = np.empty_like(x)
-    mask = x < -0.6931471805599453  # log(1/2)
-    out[mask] = np.log1p(-np.exp(x[mask]))
-    mask = ~mask
-    out[mask] = np.log(-np.expm1(x[mask]))
-    return out
-
-
 def flatten_list(tensors):
     return pt.concatenate([var.ravel() for var in tensors])
 
diff --git a/pymc/model/core.py b/pymc/model/core.py
index 469001e80..c88d4d4c6 100644
--- a/pymc/model/core.py
+++ b/pymc/model/core.py
@@ -68,7 +68,6 @@
     UNSET,
     VarName,
     WithMemoization,
-    _add_future_warning_tag,
     _UnsetType,
     get_transformed_name,
     get_value_vars_from_user_vars,
@@ -450,19 +449,12 @@ def __init__(
         coords=None,
         check_bounds=True,
         *,
-        coords_mutable=None,
         model: _UnsetType | None | Model = UNSET,
     ):
         self.name = self._validate_name(name)
         self.check_bounds = check_bounds
         self._parent = model if not isinstance(model, _UnsetType) else MODEL_MANAGER.parent_context
 
-        if coords_mutable is not None:
-            warnings.warn(
-                "All coords are now mutable by default. coords_mutable will be removed in a future release.",
-                FutureWarning,
-            )
-
         if self.parent is not None:
             self.named_vars = treedict(parent=self.parent.named_vars)
             self.named_vars_to_dims = treedict(parent=self.parent.named_vars_to_dims)
@@ -492,9 +484,6 @@ def __init__(
             self._coords = {}
             self._dim_lengths = {}
         self.add_coords(coords)
-        if coords_mutable is not None:
-            for name, values in coords_mutable.items():
-                self.add_coord(name, values, mutable=True)
 
         from pymc.printing import str_for_model
 
@@ -921,7 +910,6 @@ def add_coord(
         self,
         name: str,
         values: Sequence | np.ndarray | None = None,
-        mutable: bool | None = None,
         *,
         length: int | Variable | None = None,
     ):
@@ -935,19 +923,10 @@ def add_coord(
         values : optional, array_like
             Coordinate values or ``None`` (for auto-numbering).
             If ``None`` is passed, a ``length`` must be specified.
-        mutable : bool
-            Whether the created dimension should be resizable.
-            Default is False.
         length : optional, scalar
             A scalar of the dimensions length.
             Defaults to ``pytensor.tensor.constant(len(values))``.
         """
-        if mutable is not None:
-            warnings.warn(
-                "Coords are now always mutable. Specifying `mutable` will raise an error in a future release",
-                FutureWarning,
-            )
-
         if name in {"draw", "chain", "__sample__"}:
             raise ValueError(
                 "Dimensions can not be named `draw`, `chain` or `__sample__`, "
@@ -1223,7 +1202,6 @@ def register_rv(
         """
         name = self.name_for(name)
         rv_var.name = name
-        _add_future_warning_tag(rv_var)
 
         # Associate previously unknown dimension names with
         # the length of the corresponding RV dimension.
@@ -1435,9 +1413,6 @@ def create_value_var(
                         rv_var, *rv_var.owner.inputs
                     ).tag.test_value
 
-        _add_future_warning_tag(value_var)
-        rv_var.tag.value_var = value_var
-
         self.rvs_to_transforms[rv_var] = transform
         self.rvs_to_values[rv_var] = value_var
         self.values_to_rvs[value_var] = rv_var
@@ -1701,23 +1676,6 @@ def profile(
 
         return f.profile
 
-    def update_start_vals(self, a: dict[str, np.ndarray], b: dict[str, np.ndarray]):
-        r"""Update point `a` with `b`, without overwriting existing keys.
-
-        Values specified for transformed variables in `a` will be recomputed
-        conditional on the values of `b` and stored in `b`.
-
-        Parameters
-        ----------
-        a : dict
-
-        b : dict
-        """
-        raise FutureWarning(
-            "The `Model.update_start_vals` method was removed."
-            " To change initial values you may set the items of `Model.initial_values` directly."
-        )
-
     def eval_rv_shapes(self) -> dict[str, tuple[int, ...]]:
         """Evaluate shapes of untransformed AND transformed free variables.
 
diff --git a/pymc/plots/__init__.py b/pymc/plots/__init__.py
index 49068d536..f1c63ce58 100644
--- a/pymc/plots/__init__.py
+++ b/pymc/plots/__init__.py
@@ -19,9 +19,7 @@
 See https://arviz-devs.github.io/arviz/ for details on plots.
 """
 
-import functools
 import sys
-import warnings
 
 import arviz as az
 
@@ -32,39 +30,4 @@
         setattr(sys.modules[__name__], attr, obj)
 
 
-def alias_deprecation(func, alias: str):
-    original = func.__name__
-
-    @functools.wraps(func)
-    def wrapped(*args, **kwargs):
-        raise FutureWarning(
-            f"The function `{alias}` from PyMC was an alias for `{original}` from ArviZ. "
-            "It was removed in PyMC 4.0. "
-            f"Switch to `pymc.{original}` or `arviz.{original}`."
-        )
-
-    return wrapped
-
-
-# Aliases of ArviZ functions
-autocorrplot = alias_deprecation(az.plot_autocorr, alias="autocorrplot")
-forestplot = alias_deprecation(az.plot_forest, alias="forestplot")
-kdeplot = alias_deprecation(az.plot_kde, alias="kdeplot")
-energyplot = alias_deprecation(az.plot_energy, alias="energyplot")
-densityplot = alias_deprecation(az.plot_density, alias="densityplot")
-pairplot = alias_deprecation(az.plot_pair, alias="pairplot")
-traceplot = alias_deprecation(az.plot_trace, alias="traceplot")
-compareplot = alias_deprecation(az.plot_compare, alias="compareplot")
-
-
-__all__ = (
-    *az.plots.__all__,
-    "autocorrplot",
-    "compareplot",
-    "forestplot",
-    "kdeplot",
-    "traceplot",
-    "energyplot",
-    "densityplot",
-    "pairplot",
-)
+__all__ = az.plots.__all__
diff --git a/pymc/pytensorf.py b/pymc/pytensorf.py
index 78eb3f7bb..9608966a1 100644
--- a/pymc/pytensorf.py
+++ b/pymc/pytensorf.py
@@ -13,7 +13,7 @@
 #   limitations under the License.
 import warnings
 
-from collections.abc import Callable, Generator, Iterable, Sequence
+from collections.abc import Iterable, Sequence
 from typing import cast
 
 import numpy as np
@@ -33,7 +33,6 @@
     clone_get_equiv,
     equal_computations,
     graph_inputs,
-    walk,
 )
 from pytensor.graph.fg import FunctionGraph, Output
 from pytensor.scalar.basic import Cast
@@ -55,11 +54,9 @@
 
 PotentialShapeType = int | np.ndarray | Sequence[int | Variable] | TensorVariable
 
-
 __all__ = [
     "CallableTensor",
     "compile",
-    "compile_pymc",
     "cont_inputs",
     "convert_data",
     "convert_observed_data",
@@ -173,38 +170,6 @@ def extract_obs_data(x: TensorVariable) -> np.ndarray:
     raise TypeError(f"Data cannot be extracted from {x}")
 
 
-def walk_model(
-    graphs: Iterable[TensorVariable],
-    stop_at_vars: set[TensorVariable] | None = None,
-    expand_fn: Callable[[TensorVariable], Iterable[TensorVariable]] = lambda var: [],
-) -> Generator[TensorVariable, None, None]:
-    """Walk model graphs and yield their nodes.
-
-    Parameters
-    ----------
-    graphs
-        The graphs to walk.
-    stop_at_vars
-        A list of variables at which the walk will terminate.
-    expand_fn
-        A function that returns the next variable(s) to be traversed.
-    """
-    warnings.warn("walk_model will be removed in a future relase of PyMC", FutureWarning)
-
-    if stop_at_vars is None:
-        stop_at_vars = set()
-
-    def expand(var):
-        new_vars = expand_fn(var)
-
-        if var.owner and var not in stop_at_vars:
-            new_vars.extend(reversed(var.owner.inputs))
-
-        return new_vars
-
-    yield from walk(graphs, expand, bfs=False)
-
-
 def replace_vars_in_graphs(
     graphs: Iterable[Variable],
     replacements: dict[Variable, Variable],
@@ -954,14 +919,6 @@ def compile(
     return pytensor_function
 
 
-def compile_pymc(*args, **kwargs):
-    warnings.warn(
-        "compile_pymc was renamed to compile. Old name will be removed in a future release of PyMC",
-        FutureWarning,
-    )
-    return compile(*args, **kwargs)
-
-
 def constant_fold(
     xs: Sequence[TensorVariable], raise_not_constant: bool = True
 ) -> tuple[np.ndarray | Variable, ...]:
diff --git a/pymc/sampling/mcmc.py b/pymc/sampling/mcmc.py
index f2dfa6e9c..76695d760 100644
--- a/pymc/sampling/mcmc.py
+++ b/pymc/sampling/mcmc.py
@@ -755,11 +755,9 @@ def joined_blas_limiter():
         )
 
     if random_seed == -1:
-        warnings.warn(
-            "Setting random_seed = -1 is deprecated. Pass `None` to not specify a seed.",
-            FutureWarning,
+        raise ValueError(
+            "Setting random_seed = -1 is not allowed. Pass `None` to not specify a seed."
         )
-        random_seed = None
     elif isinstance(random_seed, tuple | list):
         warnings.warn(
             "A list or tuple of random_seed no longer specifies the specific random_seed of each chain. "
diff --git a/pymc/testing.py b/pymc/testing.py
index a5fdc2832..b016c25ad 100644
--- a/pymc/testing.py
+++ b/pymc/testing.py
@@ -13,7 +13,6 @@
 #   limitations under the License.
 import functools as ft
 import itertools as it
-import warnings
 
 from collections.abc import Callable, Sequence
 from typing import Any
@@ -665,14 +664,6 @@ def check_selfconsistency_discrete_logcdf(
             )
 
 
-def assert_moment_is_expected(model, expected, check_finite_logp=True):
-    warnings.warn(
-        "assert_moment_is_expected is deprecated. Use assert_support_point_is_expected instead.",
-        FutureWarning,
-    )
-    assert_support_point_is_expected(model, expected, check_finite_logp=check_finite_logp)
-
-
 def assert_support_point_is_expected(model, expected, check_finite_logp=True):
     fn = make_initial_point_fn(
         model=model,
diff --git a/pymc/util.py b/pymc/util.py
index 224ce0fa9..ad9256add 100644
--- a/pymc/util.py
+++ b/pymc/util.py
@@ -14,7 +14,6 @@
 
 import functools
 import re
-import warnings
 
 from collections import namedtuple
 from collections.abc import Iterable, Sequence
@@ -29,7 +28,6 @@
 from cachetools import LRUCache, cachedmethod
 from pytensor import Variable
 from pytensor.compile import SharedVariable
-from pytensor.graph.utils import ValidatingScratchpad
 from rich.box import SIMPLE_HEAD
 from rich.console import Console
 from rich.progress import (
@@ -60,19 +58,6 @@
 ]
 
 
-def __getattr__(name):
-    if name == "dataset_to_point_list":
-        warnings.warn(
-            f"{name} has been moved to backends.arviz. Importing from util will fail in a future release.",
-            FutureWarning,
-        )
-        from pymc.backends.arviz import dataset_to_point_list
-
-        return dataset_to_point_list
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")
-
-
 VarName = NewType("VarName", str)
 
 default_progress_theme = Theme(
@@ -540,34 +525,6 @@ def get_value_vars_from_user_vars(vars: Variable | Sequence[Variable], model) ->
     return value_vars
 
 
-class _FutureWarningValidatingScratchpad(ValidatingScratchpad):
-    def __getattribute__(self, name):
-        for deprecated_names, alternative in (
-            (("value_var", "observations"), "model.rvs_to_values[rv]"),
-            (("transform",), "model.rvs_to_transforms[rv]"),
-        ):
-            if name in deprecated_names:
-                try:
-                    super().__getattribute__(name)
-                except AttributeError:
-                    pass
-                else:
-                    warnings.warn(
-                        f"The tag attribute {name} is deprecated. Use {alternative} instead",
-                        FutureWarning,
-                    )
-        return super().__getattribute__(name)
-
-
-def _add_future_warning_tag(var) -> None:
-    old_tag = var.tag
-    if not isinstance(old_tag, _FutureWarningValidatingScratchpad):
-        new_tag = _FutureWarningValidatingScratchpad("test_value", var.type.filter)
-        for k, v in old_tag.__dict__.items():
-            new_tag.__dict__.setdefault(k, v)
-        var.tag = new_tag
-
-
 def makeiter(a):
     if isinstance(a, tuple | list):
         return a
diff --git a/tests/distributions/test_custom.py b/tests/distributions/test_custom.py
index dba68c26e..7e05b2d02 100644
--- a/tests/distributions/test_custom.py
+++ b/tests/distributions/test_custom.py
@@ -196,15 +196,6 @@ def test_custom_dist_default_support_point_univariate(self, support_point, size,
         assert isinstance(x.owner.op, CustomDistRV)
         assert_support_point_is_expected(model, expected, check_finite_logp=False)
 
-    def test_custom_dist_moment_future_warning(self):
-        moment = lambda rv, size, *rv_inputs: 5 * pt.ones(size, dtype=rv.dtype)  # noqa: E731
-        with Model() as model:
-            with pytest.warns(
-                FutureWarning, match="`moment` argument is deprecated. Use `support_point` instead."
-            ):
-                x = CustomDist("x", moment=moment, size=())
-        assert_support_point_is_expected(model, 5, check_finite_logp=False)
-
     @pytest.mark.parametrize("size", [(), (2,), (3, 2)], ids=str)
     def test_custom_dist_custom_support_point_univariate(self, size):
         def density_support_point(rv, size, mu):
diff --git a/tests/distributions/test_dist_math.py b/tests/distributions/test_dist_math.py
index 39b9cfdd0..ff213954a 100644
--- a/tests/distributions/test_dist_math.py
+++ b/tests/distributions/test_dist_math.py
@@ -30,7 +30,6 @@
     clipped_beta_rvs,
     factln,
     i0e,
-    incomplete_beta,
     multigammaln,
 )
 from pymc.logprob.utils import ParameterValueError
@@ -176,9 +175,3 @@ def ref_multigammaln(a, b):
         for x in xvals:
             if np.all(x > 0.5 * (p - 1)):
                 check_vals(multigammaln_, ref_multigammaln, x, p)
-
-
-def test_incomplete_beta_deprecation():
-    with pytest.warns(FutureWarning, match="incomplete_beta has been deprecated"):
-        res = incomplete_beta(3, 5, 0.5).eval()
-    assert np.isclose(res, pt.betainc(3, 5, 0.5).eval())
diff --git a/tests/distributions/test_distribution.py b/tests/distributions/test_distribution.py
index 405946544..f84fb8f86 100644
--- a/tests/distributions/test_distribution.py
+++ b/tests/distributions/test_distribution.py
@@ -52,7 +52,6 @@
     check_logcdf,
     check_logp,
 )
-from pymc.util import _FutureWarningValidatingScratchpad
 
 
 class TestBugfixes:
@@ -239,43 +238,6 @@ def rv_op(cls, size=None, rng=None):
         assert resized_rv.type.shape == (5,)
 
 
-def test_tag_future_warning_dist():
-    # Test no unexpected warnings
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-
-        x = pm.Normal.dist()
-        assert isinstance(x.tag, _FutureWarningValidatingScratchpad)
-
-        x.tag.banana = "banana"
-        assert x.tag.banana == "banana"
-
-        # Check we didn't break test_value filtering
-        x.tag.test_value = np.array(1)
-        assert x.tag.test_value == 1
-        with pytest.raises(TypeError, match="Wrong number of dimensions"):
-            x.tag.test_value = np.array([1, 1])
-        assert x.tag.test_value == 1
-
-        # No warning if deprecated attribute is not present
-        with pytest.raises(AttributeError):
-            x.tag.value_var
-
-        # Warning if present
-        x.tag.value_var = "1"
-        with pytest.warns(FutureWarning, match="Use model.rvs_to_values"):
-            value_var = x.tag.value_var
-        assert value_var == "1"
-
-        # Check that PyMC method that copies tag contents does not erase special tag
-        new_x = change_dist_size(x, new_size=5)
-        assert new_x.tag is not x.tag
-        assert isinstance(new_x.tag, _FutureWarningValidatingScratchpad)
-        with pytest.warns(FutureWarning, match="Use model.rvs_to_values"):
-            value_var = new_x.tag.value_var
-        assert value_var == "1"
-
-
 def test_distribution_op_registered():
     """Test that returned Ops are registered as virtual subclasses of the respective PyMC distributions."""
     assert isinstance(Normal.dist().owner.op, Normal)
diff --git a/tests/distributions/test_timeseries.py b/tests/distributions/test_timeseries.py
index 197296a5a..e9d3e7615 100644
--- a/tests/distributions/test_timeseries.py
+++ b/tests/distributions/test_timeseries.py
@@ -461,26 +461,6 @@ def test_mvstudentt(self, param):
         assert isinstance(init_dist.owner.op, Dirichlet)
         assert isinstance(innovation_dist.owner.op, MvStudentT)
 
-    @pytest.mark.parametrize(
-        "distribution, init_dist, build_kwargs",
-        [
-            (GaussianRandomWalk, Normal.dist(), {}),
-            (
-                MvGaussianRandomWalk,
-                Dirichlet.dist(np.ones(3)),
-                {"mu": np.zeros(3), "tau": np.eye(3)},
-            ),
-            (
-                MvStudentTRandomWalk,
-                Dirichlet.dist(np.ones(3)),
-                {"nu": 4, "mu": np.zeros(3), "tau": np.eye(3)},
-            ),
-        ],
-    )
-    def test_init_deprecated_arg(self, distribution, init_dist, build_kwargs):
-        with pytest.warns(FutureWarning, match="init parameter is now called init_dist"):
-            distribution.dist(init=init_dist, steps=10, **build_kwargs)
-
 
 class TestAR:
     def test_order1_logp(self):
@@ -713,10 +693,6 @@ def test_support_point(self, size, expected):
             AR("x", rho=[0, 0], init_dist=init_dist, steps=5, size=size)
         assert_support_point_is_expected(model, expected, check_finite_logp=False)
 
-    def test_init_deprecated_arg(self):
-        with pytest.warns(FutureWarning, match="init parameter is now called init_dist"):
-            AR.dist(rho=[1, 2, 3], init=Normal.dist(), shape=(10,))
-
     def test_change_dist_size(self):
         base_dist = AR.dist(rho=[0.5, 0.5], init_dist=pm.Normal.dist(size=(2,)), shape=(3, 10))
 
diff --git a/tests/distributions/test_transform.py b/tests/distributions/test_transform.py
index e28052bab..26bc8b1bf 100644
--- a/tests/distributions/test_transform.py
+++ b/tests/distributions/test_transform.py
@@ -151,9 +151,6 @@ def test_sum_to_1():
     check_vector_transform(tr.sum_to_1, Simplex(2))
     check_vector_transform(tr.sum_to_1, Simplex(4))
 
-    with pytest.warns(FutureWarning, match="ndim_supp argument is deprecated"):
-        tr.SumTo1(2)
-
     check_jacobian_det(
         tr.sum_to_1,
         Vector(Unit, 2),
@@ -161,13 +158,6 @@ def test_sum_to_1():
         floatX(np.array([0, 0])),
         lambda x: x[:-1],
     )
-    check_jacobian_det(
-        tr.multivariate_sum_to_1,
-        Vector(Unit, 2),
-        pt.vector,
-        floatX(np.array([0, 0])),
-        lambda x: x[:-1],
-    )
 
 
 def test_log():
@@ -271,9 +261,6 @@ def test_circular():
 def test_ordered():
     check_vector_transform(tr.ordered, SortedVector(6))
 
-    with pytest.warns(FutureWarning, match="ndim_supp argument is deprecated"):
-        tr.Ordered(1)
-
     check_jacobian_det(
         tr.ordered, Vector(R, 2), pt.vector, floatX(np.array([0, 0])), elemwise=False
     )
@@ -678,23 +665,3 @@ def log_jac_det(self, value, *inputs):
             match="are not allowed to broadcast together. There is a bug in the implementation of either one",
         ):
             m.logp(jacobian=jacobian_val)
-
-
-def test_deprecated_ndim_supp_transforms():
-    with pytest.warns(FutureWarning, match="deprecated"):
-        tr.Ordered(ndim_supp=1)
-
-    with pytest.warns(FutureWarning, match="deprecated"):
-        assert tr.univariate_ordered == tr.ordered
-
-    with pytest.warns(FutureWarning, match="deprecated"):
-        assert tr.multivariate_ordered == tr.ordered
-
-    with pytest.warns(FutureWarning, match="deprecated"):
-        tr.SumTo1(ndim_supp=1)
-
-    with pytest.warns(FutureWarning, match="deprecated"):
-        assert tr.univariate_sum_to_1 == tr.sum_to_1
-
-    with pytest.warns(FutureWarning, match="deprecated"):
-        assert tr.multivariate_sum_to_1 == tr.sum_to_1
diff --git a/tests/logprob/test_basic.py b/tests/logprob/test_basic.py
index f6014d78a..1bd103ce1 100644
--- a/tests/logprob/test_basic.py
+++ b/tests/logprob/test_basic.py
@@ -201,7 +201,7 @@ def test_persist_inputs():
     assert y_vv_2 in ancestors([logp_2_combined])
 
 
-def test_warn_random_found_factorized_joint_logprob():
+def test_warn_rvs_conditional_logp():
     x_rv = pt.random.normal(name="x")
     y_rv = pt.random.normal(x_rv, 1, name="y")
 
@@ -369,7 +369,7 @@ def test_probability_inference_fails(func, func_name):
         (icdf, "ppf", 0.7),
     ],
 )
-def test_warn_random_found_probability_inference(func, scipy_func, test_value):
+def test_warn_rvs_probability_derivation(func, scipy_func, test_value):
     # Fail if unexpected warning is issued
     with warnings.catch_warnings():
         warnings.simplefilter("error")
diff --git a/tests/model/test_core.py b/tests/model/test_core.py
index b26a9d96b..a12046c31 100644
--- a/tests/model/test_core.py
+++ b/tests/model/test_core.py
@@ -56,7 +56,6 @@
 from pymc.logprob.basic import transformed_conditional_logp
 from pymc.logprob.transforms import IntervalTransform
 from pymc.model import Point, ValueGradFunction, modelcontext
-from pymc.util import _FutureWarningValidatingScratchpad
 from pymc.variational.minibatch_rv import MinibatchRandomVariable
 from tests.models import simple_model
 
@@ -1648,55 +1647,6 @@ def test_deterministic(self):
             )
 
 
-def test_tag_future_warning_model():
-    # Test no unexpected warnings
-    with warnings.catch_warnings():
-        warnings.simplefilter("error")
-
-        model = pm.Model()
-
-        x = pt.random.normal()
-        x.tag.something_else = "5"
-        x.tag.test_value = 0
-        assert not isinstance(x.tag, _FutureWarningValidatingScratchpad)
-
-        # Test that model changes the tag type, but copies existing contents
-        x = model.register_rv(x, name="x", transform=log)
-        assert isinstance(x.tag, _FutureWarningValidatingScratchpad)
-        assert x.tag.something_else == "5"
-        assert x.tag.test_value == 0
-
-        # Test expected warnings
-        with pytest.warns(FutureWarning, match="model.rvs_to_values"):
-            x_value = x.tag.value_var
-
-        assert isinstance(x_value.tag, _FutureWarningValidatingScratchpad)
-        with pytest.warns(FutureWarning, match="model.rvs_to_transforms"):
-            transform = x_value.tag.transform
-        assert transform is log
-
-        with pytest.raises(AttributeError):
-            x.tag.observations
-
-        # Cloning a node will keep the same tag type and contents
-        y = x.owner.clone().default_output()
-        assert y is not x
-        assert y.tag is not x.tag
-        assert isinstance(y.tag, _FutureWarningValidatingScratchpad)
-        y = model.register_rv(y, name="y", observed=5)
-        assert isinstance(y.tag, _FutureWarningValidatingScratchpad)
-
-        # Test expected warnings
-        with pytest.warns(FutureWarning, match="model.rvs_to_values"):
-            y_value = y.tag.value_var
-        with pytest.warns(FutureWarning, match="model.rvs_to_values"):
-            y_obs = y.tag.observations
-        assert y_value is y_obs
-        assert y_value.eval() == 5
-
-        assert isinstance(y_value.tag, _FutureWarningValidatingScratchpad)
-
-
 class TestModelDebug:
     @pytest.mark.parametrize("fn", ("logp", "dlogp", "random"))
     def test_no_problems(self, fn, capfd):
diff --git a/tests/model/test_fgraph.py b/tests/model/test_fgraph.py
index 178eb3968..a3f04e3ce 100644
--- a/tests/model/test_fgraph.py
+++ b/tests/model/test_fgraph.py
@@ -108,7 +108,7 @@ def test_data(inline_views):
     with pm.Model(coords={"test_dim": range(3)}) as m_old:
         x = pm.Data("x", [0.0, 1.0, 2.0], dims=("test_dim",))
         y = pm.Data("y", [10.0, 11.0, 12.0], dims=("test_dim",))
-        sigma = pm.MutableData("sigma", [1.0], shape=(1,))
+        sigma = pm.Data("sigma", [1.0], shape=(1,))
         b0 = pm.Data("b0", np.zeros((1,)), shape=((1,)))
         b1 = pm.Normal("b1", 1.0, sigma=1e-8)
         mu = pm.Deterministic("mu", b0 + b1 * x, dims=("test_dim",))
diff --git a/tests/model/transform/test_conditioning.py b/tests/model/transform/test_conditioning.py
index 828fac737..fa9ce7124 100644
--- a/tests/model/transform/test_conditioning.py
+++ b/tests/model/transform/test_conditioning.py
@@ -29,6 +29,7 @@
     observe,
     remove_value_transforms,
 )
+from pymc.model.transform.optimization import freeze_dims_and_data
 from pymc.variational.minibatch_rv import create_minibatch_rv
 
 
@@ -176,10 +177,13 @@ def test_do_posterior_predictive():
 def test_do_constant(mutable):
     rng = np.random.default_rng(seed=122)
     with pm.Model() as m:
-        x = pm.Data("x", 0, mutable=mutable)
+        x = pm.Data("x", 0)
         y = pm.Normal("y", x, 1e-3)
 
-    do_m = do(m, {x: 105})
+    if not mutable:
+        m = freeze_dims_and_data(m, data=["x"])
+
+    do_m = do(m, {m["x"]: 105})
     assert pm.draw(do_m["y"], random_seed=rng) > 100
 
 
diff --git a/tests/test_initial_point.py b/tests/test_initial_point.py
index bda39bd1f..e13ed5279 100644
--- a/tests/test_initial_point.py
+++ b/tests/test_initial_point.py
@@ -294,15 +294,6 @@ class MyNormalDistribution(pm.Normal):
 
         assert np.isclose(res["x"], np.pi)
 
-    def test_future_warning_moment(self):
-        with pm.Model() as m:
-            pm.Normal("x", initval="moment")
-            with pytest.warns(
-                FutureWarning,
-                match="The 'moment' strategy is deprecated. Use 'support_point' instead.",
-            ):
-                ip = m.initial_point(random_seed=42)
-
 
 def test_pickling_issue_5090():
     with pm.Model() as model:
diff --git a/tests/test_math.py b/tests/test_math.py
index 3f811fc2b..eeee1f164 100644
--- a/tests/test_math.py
+++ b/tests/test_math.py
@@ -15,7 +15,6 @@
 import warnings
 
 import numpy as np
-import numpy.testing as npt
 import pytensor
 import pytensor.tensor as pt
 import pytest
@@ -29,10 +28,8 @@
     kron_solve_lower,
     kronecker,
     log1mexp,
-    log1mexp_numpy,
     logdet,
     logdiffexp,
-    logdiffexp_numpy,
     probit,
 )
 from pymc.pytensorf import floatX
@@ -126,70 +123,22 @@ def test_probit():
     np.testing.assert_allclose(invprobit(probit(p)).eval(), p, atol=1e-5)
 
 
-def test_log1mexp():
-    vals = np.array([-1, 0, 1e-20, 1e-4, 10, 100, 1e20])
-    vals_ = vals.copy()
-    # import mpmath
-    # mpmath.mp.dps = 1000
-    # [float(mpmath.log(1 - mpmath.exp(-x))) for x in vals]
-    expected = np.array(
-        [
-            np.nan,
-            -np.inf,
-            -46.051701859880914,
-            -9.210390371559516,
-            -4.540096037048921e-05,
-            -3.720075976020836e-44,
-            0.0,
-        ]
-    )
-    actual = pt.log1mexp(-vals).eval()
-    npt.assert_allclose(actual, expected)
-
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", "divide by zero encountered in log", RuntimeWarning)
-        warnings.filterwarnings("ignore", "invalid value encountered in log", RuntimeWarning)
-        with pytest.warns(FutureWarning, match="deprecated"):
-            actual_ = log1mexp_numpy(-vals, negative_input=True)
-    npt.assert_allclose(actual_, expected)
-    # Check that input was not changed in place
-    npt.assert_allclose(vals, vals_)
-
-
-@pytest.mark.filterwarnings("error")
-def test_log1mexp_numpy_no_warning():
-    """Assert RuntimeWarning is not raised for very small numbers"""
-    with pytest.warns(FutureWarning, match="deprecated"):
-        log1mexp_numpy(-1e-25, negative_input=True)
-
-
-def test_log1mexp_numpy_integer_input():
-    with pytest.warns(FutureWarning, match="deprecated"):
-        assert np.isclose(log1mexp_numpy(-2, negative_input=True), pt.log1mexp(-2).eval())
-
-
 @pytest.mark.filterwarnings("error")
 def test_log1mexp_deprecation_warnings():
-    with pytest.warns(FutureWarning, match="deprecated"):
-        with pytest.warns(
-            FutureWarning,
-            match="pymc.math.log1mexp_numpy will expect a negative input",
-        ):
-            res_pos = log1mexp_numpy(2)
-
-        res_neg = log1mexp_numpy(-2, negative_input=True)
+    with pytest.raises(
+        ValueError,
+        match="log1mexp with negative_input=False is no longer supported",
+    ):
+        log1mexp(2, negative_input=False).eval()
 
-        with pytest.warns(
-            FutureWarning,
-            match="pymc.math.log1mexp will expect a negative input",
-        ):
-            res_pos_at = log1mexp(2).eval()
+    with pytest.warns(FutureWarning):
+        res_1 = log1mexp(-2, negative_input=True).eval()
 
-        res_neg_at = log1mexp(-2, negative_input=True).eval()
+    res_2 = log1mexp(-2).eval()
+    res_ref = pt.log1mexp(-2).eval()
 
-    assert np.isclose(res_pos, res_neg)
-    assert np.isclose(res_pos_at, res_neg)
-    assert np.isclose(res_neg_at, res_neg)
+    assert np.isclose(res_ref, res_1)
+    assert np.isclose(res_ref, res_2)
 
 
 def test_logdiffexp():
@@ -197,8 +146,6 @@ def test_logdiffexp():
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore", "divide by zero encountered in log", RuntimeWarning)
         b = np.log([0, 1, 2, 3])
-    with pytest.warns(FutureWarning, match="deprecated"):
-        assert np.allclose(logdiffexp_numpy(a, b), 0)
     assert np.allclose(logdiffexp(a, b).eval(), 0)
 
 
diff --git a/tests/test_pytensorf.py b/tests/test_pytensorf.py
index 34360397a..580c29344 100644
--- a/tests/test_pytensorf.py
+++ b/tests/test_pytensorf.py
@@ -25,7 +25,6 @@
 from pytensor.compile import UnusedInputError
 from pytensor.compile.builders import OpFromGraph
 from pytensor.graph.basic import Variable, equal_computations
-from pytensor.tensor.random.basic import normal, uniform
 from pytensor.tensor.subtensor import AdvancedIncSubtensor
 
 import pymc as pm
@@ -46,7 +45,6 @@
     replace_rng_nodes,
     replace_vars_in_graphs,
     reseed_rngs,
-    walk_model,
 )
 from pymc.vartypes import int_types
 
@@ -283,42 +281,7 @@ def test_pandas_to_array_pandas_index():
     np.testing.assert_array_equal(result, expected)
 
 
-def test_walk_model():
-    a = pt.vector("a")
-    b = uniform(0.0, a, name="b")
-    c = pt.log(b)
-    c.name = "c"
-    d = pt.vector("d")
-    e = normal(c, d, name="e")
-
-    test_graph = pt.exp(e + 1)
-
-    with pytest.warns(FutureWarning):
-        res = list(walk_model((test_graph,)))
-    assert a in res
-    assert b in res
-    assert c in res
-    assert d in res
-    assert e in res
-
-    with pytest.warns(FutureWarning):
-        res = list(walk_model((test_graph,), stop_at_vars={c}))
-    assert a not in res
-    assert b not in res
-    assert c in res
-    assert d in res
-    assert e in res
-
-    with pytest.warns(FutureWarning):
-        res = list(walk_model((test_graph,), stop_at_vars={b}))
-    assert a not in res
-    assert b in res
-    assert c in res
-    assert d in res
-    assert e in res
-
-
-class TestCompilePyMC:
+class TestCompile:
     def test_check_bounds_flag(self):
         """Test that CheckParameterValue Ops are replaced or removed when using compile_pymc"""
         logp = pt.ones(3)