diff --git a/src/trackers.py b/src/trackers.py
index 555e14b4ce30920a7d6803a09a579071a2a92057..9f4ed2f5da9bc7c4cbdf42b6b80d1791f6d90066 100644
--- a/src/trackers.py
+++ b/src/trackers.py
@@ -804,29 +804,3 @@ def get_association_matrix(meas, tracks, logic_params, gater):
         py = stats.multivariate_normal.pdf(val_meas.squeeze().T, mean=yhat.flatten(), cov=H@track['P'][-1]@H.T+track['filt'].sensor_model['R'])
         association_matrix[validation_matrix[:, ti], ti] = np.log(track['filt'].sensor_model['PD']*py/(1-track['filt'].sensor_model['PD'])) # PG assumed = 1
     return association_matrix, validation_matrix
-
-### Obsolete
-def compute_prob(association_matrix, validation_matrix, logic_params):
-    # Association matrix is assumed to consist of tracks and FA, no NT.
-    ny = association_matrix.shape[0]
-    ntracks = association_matrix.shape[1]-ny
-    P = np.zeros((ny, ntracks))
-
-    def rec_find_associations(association_matrix, assoc_done, logic_params):
-        inds = np.where(association_matrix[0, :] != -np.inf)[0] # These are the nodes necessary to look at
-        this_assoc = []
-        for k, i in enumerate(inds):
-            if i not in assoc_done:
-                if association_matrix.shape[0] != 1:
-                    assoc = rec_compute_prob(association_matrix[1:, :], [[i]], logic_params)
-                    this_assoc.extend(assoc)
-                else:
-                    this_assoc.append([i])
-        result = []
-        for assoc in assoc_done:
-            for th_assoc in this_assoc:
-                result.append(assoc + th_assoc)
-        return result
-
-    possible_associations = rec_find_associations(association_matrix, [[]], logic_params) # Recursively finds possible measurement hypothesis
-    return res