Avoid flakyness of GP Latent Vs Marginal introduced by chol numerics

pymc-devs · Nov 10, 2024 · 0ae2618 · 0ae2618
1 parent 65ab162
commit 0ae2618
Showing 1 changed file with 1 addition and 1 deletion.
diff --git a/tests/gp/test_gp.py b/tests/gp/test_gp.py
@@ -377,7 +377,7 @@ def testLatentMultioutput(self):
             cov_func = pm.gp.cov.ExpQuad(3, [0.1, 0.2, 0.3])
             mean_func = pm.gp.mean.Constant(0.5)
             latent_gp = pm.gp.Latent(mean_func=mean_func, cov_func=cov_func)
-            latent_f = latent_gp.prior("f", X, n_outputs=n_outputs, reparameterize=True)
+            latent_f = latent_gp.prior("f", X, n_outputs=n_outputs, reparameterize=False)
             latent_p = latent_gp.conditional("p", Xnew)
 
         with pm.Model() as marginal_model: