diff --git a/meridian/model/eda/eda_engine.py b/meridian/model/eda/eda_engine.py
index c0ef805a4..e1aafd41f 100644
--- a/meridian/model/eda/eda_engine.py
+++ b/meridian/model/eda/eda_engine.py
@@ -2477,46 +2477,3 @@ def check_population_corr_raw_media(
         explanation=eda_constants.POPULATION_CORRELATION_RAW_MEDIA_INFO,
         check_name='check_population_corr_raw_media',
     )
-
-  def _check_prior_probability(
-      self,
-  ) -> eda_outcome.EDAOutcome[eda_outcome.PriorProbabilityArtifact]:
-    """Checks the prior probability of a negative baseline.
-
-    Returns:
-      An EDAOutcome object containing a PriorProbabilityArtifact. The artifact
-      includes a mock prior negative baseline probability and a DataArray of
-      mock mean prior contributions per channel.
-    """
-    # TODO: b/476128592 - currently, this check is blocked. for the meantime,
-    # we will return mock data for the report.
-    channel_names = self._model_context.input_data.get_all_channels()
-    mean_prior_contribution = np.random.uniform(
-        size=len(channel_names), low=0.0, high=0.05
-    )
-    mean_prior_contribution_da = xr.DataArray(
-        mean_prior_contribution,
-        coords={constants.CHANNEL: channel_names},
-        dims=[constants.CHANNEL],
-    )
-
-    artifact = eda_outcome.PriorProbabilityArtifact(
-        level=eda_outcome.AnalysisLevel.OVERALL,
-        prior_negative_baseline_prob=0.123,
-        mean_prior_contribution_da=mean_prior_contribution_da,
-    )
-
-    findings = [
-        eda_outcome.EDAFinding(
-            severity=eda_outcome.EDASeverity.INFO,
-            explanation=eda_constants.PRIOR_PROBABILITY_REPORT_INFO,
-            finding_cause=eda_outcome.FindingCause.NONE,
-            associated_artifact=artifact,
-        )
-    ]
-
-    return eda_outcome.EDAOutcome(
-        check_type=eda_outcome.EDACheckType.PRIOR_PROBABILITY,
-        findings=findings,
-        analysis_artifacts=[artifact],
-    )