diff --git a/causing/model.py b/causing/model.py
index e0a6189..89e512c 100644
--- a/causing/model.py
+++ b/causing/model.py
@@ -47,7 +47,6 @@ def __post_init__(self):
             self.graph.add_node(var)
         self.trans_graph = networkx.transitive_closure(self.graph, reflexive=True)
 
-    @np.errstate(all="raise")
     def compute(
         self,
         xdat: np.array,
@@ -59,7 +58,7 @@ def compute(
         fixed_to_yind: int = None,
         fixed_vals: list = None,
         # override default parameter values
-        parameters: dict[str, float] = {},
+        parameters: dict[str, float] | None = None,
     ) -> np.array:
         """Compute y values for given x values
 
@@ -69,16 +68,19 @@ def compute(
         assert xdat.ndim == 2, f"xdat must be m*tau (is {xdat.ndim}-dimensional)"
         assert xdat.shape[0] == self.mdim, f"xdat must be m*tau (is {xdat.shape})"
         tau = xdat.shape[1]
+        if parameters is None:
+            parameters = {}
         parameters = self.parameters | parameters
 
-        yhat = np.array([[float("nan")] * tau] * len(self.yvars))
-        for i, eq in enumerate(self._model_lam):
-            if fixed_yind == i:
-                yhat[i, :] = fixed_yval
-            else:
-                eq_inputs = np.array(
-                    [[*xval, *yval] for xval, yval in zip(xdat.T, yhat.T)]
-                )
+        with np.errstate(all="raise"):
+            yhat = np.array([[float("nan")] * tau] * len(self.yvars))
+            for i, eq in enumerate(self._model_lam):
+                if fixed_yind == i:
+                    yhat[i, :] = fixed_yval
+                else:
+                    eq_inputs = np.array(
+                        [[*xval, *yval] for xval, yval in zip(xdat.T, yhat.T)]
+                    )
                 if fixed_to_yind == i:
                     eq_inputs[:, fixed_from_ind] = fixed_vals