diff --git a/docs/tutorials/notebooks b/docs/tutorials/notebooks
index 42d7ee0f..5c4c0108 160000
--- a/docs/tutorials/notebooks
+++ b/docs/tutorials/notebooks
@@ -1 +1 @@
-Subproject commit 42d7ee0fb8ede8167d65b577526af895e78a2436
+Subproject commit 5c4c010820cbaaeac57e7eafe8de92ee2e0d5da1
diff --git a/pertpy/tools/_coda/_tasccoda.py b/pertpy/tools/_coda/_tasccoda.py
index ce416006..fc0c5abe 100644
--- a/pertpy/tools/_coda/_tasccoda.py
+++ b/pertpy/tools/_coda/_tasccoda.py
@@ -149,7 +149,9 @@ def prepare(
             pen_args: Dictionary with penalty arguments.
                 With `reg="scaled_3"`, the parameters phi (aggregation bias), lambda_1, lambda_0 can be set here.
                 See the tascCODA paper for an explanation of these parameters.
-                Default: lambda_0 = 50, lambda_1 = 5, phi = 0.
+                `theta` is the global spike-and-slab mixing weight that sets the selection threshold; it is held fixed rather than sampled, since its posterior is weakly identified and collapses toward 0 under HMC/NUTS (issue #1015).
+                Lower `theta` raises the threshold and selects fewer effects; 0.5 matches the reference implementation's operating point.
+                Default: lambda_0 = 50, lambda_1 = 5, phi = 0, theta = 0.5.
             modality_key: If data is a MuData object, specify key to the aggregated sample-level AnnData object in the MuData object.
 
         Returns:
@@ -269,6 +271,9 @@ def prepare(
             pen_args["lambda_1"] = 5
         if "phi" not in pen_args:
             pen_args["phi"] = 0
+        # Global spike-and-slab mixing weight, held fixed because its posterior collapses under HMC/NUTS (issue #1015).
+        if "theta" not in pen_args:
+            pen_args["theta"] = 0.5
 
         adata.uns["scCODA_params"]["sslasso_pen_args"] = pen_args
 
@@ -360,7 +365,6 @@ def set_init_mcmc_states(self, rng_key: None, ref_index: np.ndarray, sample_adat
             "b_raw_0": rng.normal(0.0, 1.0, beta_nobl_size),
             "a_1": np.ones(dtype=np.float64, shape=beta_nobl_size) * 1 / lambda_1,
             "b_raw_1": rng.normal(0.0, 1.0, beta_nobl_size),
-            "theta": np.ones(dtype=np.float64, shape=1) * 0.5,
             "alpha": rng.normal(0.0, 1.0, alpha_size),
         }
 
@@ -399,9 +403,6 @@ def model(  # type: ignore
         ref_index = jnp.sort(ref_index)
         num_ref_nodes = len(ref_index)
 
-        # Size of inferred parameter matrix
-        d = D * (T - num_ref_nodes)
-
         # numpyro plates for all dimensions
         covariate_axis = npy.plate("covs", D, dim=-2)
         node_axis = npy.plate("ct", T, dim=-1)
@@ -409,8 +410,9 @@ def model(  # type: ignore
         cell_type_axis = npy.plate("ct", P, dim=-1)
         sample_axis = npy.plate("sample", N, dim=-2)
 
-        # Spike-and-slab LASSO effects
-        theta = npy.sample("theta", npd.Beta(concentration1=1.0, concentration0=d))
+        # Global spike-and-slab mixing weight, held fixed at pen_args["theta"] rather than sampled.
+        # Its Beta(1, d) posterior is weakly identified and collapses toward 0 under HMC/NUTS, which inflates the selection threshold delta until no effect is credible (issue #1015).
+        theta = npy.deterministic("theta", jnp.array(sample_adata.uns["scCODA_params"]["sslasso_pen_args"]["theta"]))
 
         with covariate_axis, node_axis_nobl:
             a_0 = npy.sample("a_0", npd.Exponential((lambda_0**2) / 2))
diff --git a/tests/tools/_coda/test_tasccoda.py b/tests/tools/_coda/test_tasccoda.py
index 3bf96ce3..ef981a46 100644
--- a/tests/tools/_coda/test_tasccoda.py
+++ b/tests/tools/_coda/test_tasccoda.py
@@ -56,6 +56,7 @@ def test_prepare(smillie_adata):
     assert "sample_counts" in mdata["coda"].obsm
     assert isinstance(mdata["coda"].obsm["sample_counts"], np.ndarray)
     assert np.sum(mdata["coda"].obsm["covariate_matrix"]) == 8
+    assert mdata["coda"].uns["scCODA_params"]["sslasso_pen_args"]["theta"] == 0.5
 
 
 def test_load_invalid_type_raises_error(smillie_adata):
@@ -80,3 +81,29 @@ def test_run_nuts(smillie_adata):
     assert "effect_df_Health[T.Non-inflamed]" in mdata["coda"].varm
     assert mdata["coda"].varm["effect_df_Health[T.Inflamed]"].shape == (51, 7)
     assert mdata["coda"].varm["effect_df_Health[T.Non-inflamed]"].shape == (51, 7)
+
+
+def test_theta_fixed_not_collapsed(smillie_adata):
+    """Regression test for #1015.
+
+    The global spike-and-slab mixing weight theta is held fixed rather than sampled.
+    A sampled theta collapses toward its Beta(1, d) prior (~0) under NUTS, which sends the selection threshold delta to infinity and makes every node non-credible.
+    Pinning theta keeps it at the configured value and the threshold finite.
+    """
+    mdata = tasccoda.load(
+        smillie_adata,
+        type="sample_level",
+        levels_agg=["Major_l1", "Major_l2", "Major_l3", "Major_l4", "Cluster"],
+        key_added="lineage",
+        add_level_name=True,
+    )
+    mdata = tasccoda.prepare(
+        mdata, formula="Health", reference_cell_type="automatic", tree_key="lineage", pen_args={"phi": 0, "theta": 0.3}
+    )
+    tasccoda.run_nuts(mdata, num_samples=1000, num_warmup=100, rng_key=42)
+
+    theta_samples = np.asarray(mdata["coda"].uns["scCODA_params"]["mcmc"]["samples"]["theta"])
+    assert np.allclose(theta_samples, 0.3)
+
+    node_df = tasccoda.get_node_df(mdata)
+    assert np.isfinite(node_df["Delta"]).all()