diff --git a/pymc_experimental/distributions/timeseries.py b/pymc_experimental/distributions/timeseries.py
index ddd5ff16..b591f7b0 100644
--- a/pymc_experimental/distributions/timeseries.py
+++ b/pymc_experimental/distributions/timeseries.py
@@ -19,7 +19,6 @@
 )
 from pymc.logprob.abstract import _logprob
 from pymc.logprob.basic import logp
-from pymc.logprob.utils import ignore_logprob
 from pymc.pytensorf import intX
 from pymc.util import check_dist_not_registered
 from pytensor.graph.basic import Node
@@ -166,9 +165,6 @@ def dist(cls, P=None, logit_P=None, steps=None, init_dist=None, n_lags=1, **kwar
             k = P.shape[-1]
             init_dist = pm.Categorical.dist(p=pt.full((k,), 1 / k))
 
-        # We can ignore init_dist, as it will be accounted for in the logp term
-        init_dist = ignore_logprob(init_dist)
-
         return super().dist([P, steps, init_dist], n_lags=n_lags, **kwargs)
 
     @classmethod
diff --git a/pymc_experimental/marginal_model.py b/pymc_experimental/marginal_model.py
index ee74ae00..5596c4a0 100644
--- a/pymc_experimental/marginal_model.py
+++ b/pymc_experimental/marginal_model.py
@@ -6,8 +6,8 @@
 from pymc import SymbolicRandomVariable
 from pymc.distributions.discrete import Bernoulli, Categorical, DiscreteUniform
 from pymc.distributions.transforms import Chain
-from pymc.logprob.abstract import _get_measurable_outputs, _logprob
-from pymc.logprob.basic import factorized_joint_logprob
+from pymc.logprob.abstract import _logprob
+from pymc.logprob.basic import conditional_logp
 from pymc.logprob.transforms import IntervalTransform
 from pymc.model import Model
 from pymc.pytensorf import constant_fold, inputvars
@@ -371,12 +371,6 @@ def replace_finite_discrete_marginal_subgraph(fgraph, rv_to_marginalize, all_rvs
     return rvs_to_marginalize, marginalized_rvs
 
 
-@_get_measurable_outputs.register(FiniteDiscreteMarginalRV)
-def _get_measurable_outputs_finite_discrete_marginal_rv(op, node):
-    # Marginalized RVs are not measurable
-    return node.outputs[1:]
-
-
 def get_domain_of_finite_discrete_rv(rv: TensorVariable) -> Tuple[int, ...]:
     op = rv.owner.op
     if isinstance(op, Bernoulli):
@@ -403,7 +397,7 @@ def finite_discrete_marginal_rv_logp(op, values, *inputs, **kwargs):
 
     # Obtain the joint_logp graph of the inner RV graph
     inner_rvs_to_values = {rv: rv.clone() for rv in inner_rvs}
-    logps_dict = factorized_joint_logprob(rv_values=inner_rvs_to_values, **kwargs)
+    logps_dict = conditional_logp(rv_values=inner_rvs_to_values, **kwargs)
 
     # Reduce logp dimensions corresponding to broadcasted variables
     joint_logp = logps_dict[inner_rvs_to_values[marginalized_rv]]
diff --git a/requirements.txt b/requirements.txt
index f00e5caa..4b306c2f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,2 @@
-pymc>=5.4.1
+pymc>=5.5.0
 scikit-learn