diff --git a/test_codes/pymc_compare/data_generation.py b/test_codes/pymc_compare/data_generation.py
index 5c63b396bfe261abd26c0fd22b6dcb867ae408ed..077f61fbffd1f7ff9105a4c6146e213c60fc94f7 100644
--- a/test_codes/pymc_compare/data_generation.py
+++ b/test_codes/pymc_compare/data_generation.py
@@ -24,7 +24,7 @@ T = 12
 n_inputs = 3
 step_size = 0.03
 Q = np.tile(np.eye(n_inputs), (T, 1, 1))
-test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size)
+test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
 w = np.zeros(n_inputs)
 # w = np.array([4.23061493, 2.14425199, -2.1125851])
 # test.weights = w.reshape(T, n_inputs)
@@ -45,7 +45,8 @@ for _ in range(T):
 sample = test.rvs(predictors, list(range(T)))
 pickle.dump(sample, open('test_data', 'wb'))
 
-learn = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size)
+learn = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
+
 
 def wrapper(w, t):
     learn.weights = np.tile(w, (T, 1))
@@ -71,34 +72,3 @@ for t in range(T):
     LL_weights[t] = minimize(f, np.zeros(n_inputs)).x
 
 pickle.dump((samples, LL_weights), open('gibbs_posterior', 'wb'))
-
-plt.figure(figsize=(16, 9))
-for i in range(n_inputs):
-    plt.subplot(n_inputs, 1, i+1)
-    label = 'Truth' if i == 0 else None
-    plt.plot(np.arange(T), test.weights[:, i], label=label)
-    label = 'LL' if i == 0 else None
-    plt.plot(np.arange(T), LL_weights[:, i], label=label)
-    sample_mean = np.mean(samples[:, :, i], axis=0)
-    label = 'Posterior mean' if i == 0 else None
-    plt.plot(np.arange(T), sample_mean, label=label, c='g')
-    credible_interval = np.percentile(samples[:, :, i], [2.5, 97.5], axis=0)
-    plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='g')
-
-    print(i)
-    print(sample_mean)
-    print(credible_interval[1], credible_interval[0])
-
-    label = 'pymc mean' if i == 0 else None
-    plt.plot(np.arange(T), m[:, i], label=label, c='r')
-    plt.fill_between(np.arange(T), u[:, i], low[:, i], alpha=0.2, color='r')
-
-    sns.despine()
-    if i == 0:
-        plt.legend(fontsize=18, frameon=False)
-    plt.xlim(left=0, right=T)
-    # for t in takefrom:
-    #     plt.axvline(t)
-plt.tight_layout()
-plt.savefig("timepoint test")
-plt.show()
diff --git a/test_codes/pymc_compare/dynglm_optimisation_test.py b/test_codes/pymc_compare/dynglm_optimisation_test.py
index 915400ac3d264aedc28e809a02a88c525056fd68..05df6e0cd7e1e02aa462861346bcc1e721d82e8f 100644
--- a/test_codes/pymc_compare/dynglm_optimisation_test.py
+++ b/test_codes/pymc_compare/dynglm_optimisation_test.py
@@ -22,7 +22,7 @@ T = 16
 n_inputs = 3
 step_size = 0.2
 Q = np.tile(np.eye(n_inputs), (T, 1, 1))
-test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size)
+test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
 w = np.zeros(n_inputs)
 # w = np.array([4.23061493, 2.14425199, -2.1125851])
 # test.weights = w.reshape(T, n_inputs)
diff --git a/test_codes/pymc_compare/gibbs_posterior b/test_codes/pymc_compare/gibbs_posterior
index 383917f8fe897dcb0afcb2ff3bc4529ffbcc771e..9956f9734279c1c49ddf940543147afd0c02cdc8 100644
Binary files a/test_codes/pymc_compare/gibbs_posterior and b/test_codes/pymc_compare/gibbs_posterior differ
diff --git a/test_codes/pymc_compare/gibbs_sample.py b/test_codes/pymc_compare/gibbs_sample.py
index 44f5c025c4bf346b6f3a455c734a74a526fd673e..ba07e3766f0f410ce0a27ddfe6c0a2a3a2a2eeae 100644
--- a/test_codes/pymc_compare/gibbs_sample.py
+++ b/test_codes/pymc_compare/gibbs_sample.py
@@ -10,7 +10,7 @@ step_size = 0.2
 
 Q = np.tile(np.eye(n_inputs), (T, 1, 1))
 sample = pickle.load(open('test_data', 'rb'))
-learn = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size)
+learn = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
 
 
 def wrapper(w, t):
diff --git a/test_codes/pymc_compare/pymc_posterior b/test_codes/pymc_compare/pymc_posterior
index 9cd816741f1dd620ea44cde53a1254e7b47033cd..bddfe9db2d9337c1ef82131bb57af421a98b41d1 100644
Binary files a/test_codes/pymc_compare/pymc_posterior and b/test_codes/pymc_compare/pymc_posterior differ
diff --git a/test_codes/pymc_compare/test_data b/test_codes/pymc_compare/test_data
index 635eb2f04157bb19ba7638c795ef7b1ee15c39f1..0c79466950726e3dd85a57038135ee98cfc63ff4 100644
Binary files a/test_codes/pymc_compare/test_data and b/test_codes/pymc_compare/test_data differ
diff --git a/test_codes/pymc_compare/truth b/test_codes/pymc_compare/truth
index 82915e67299670118449b9a7788574ebb2bcc1ad..65de7816b3e0374a51ec2315b4f3698f4afd7f38 100644
Binary files a/test_codes/pymc_compare/truth and b/test_codes/pymc_compare/truth differ