diff --git a/scatrex/models/cna/node.py b/scatrex/models/cna/node.py
index 14c947f..134ed86 100644
--- a/scatrex/models/cna/node.py
+++ b/scatrex/models/cna/node.py
@@ -310,6 +310,32 @@ def reset_variational_parameters(self):
             self.params = [self.variational_parameters["kernel"]["state"]["mean"], 
                         jnp.exp(self.variational_parameters["kernel"]["direction"]["log_alpha"]-self.variational_parameters["kernel"]["direction"]["log_beta"])]
 
+    def reset_variational_kernel(self, log_std=-4, init_concentration=10):
+        parent = self.parent()
+        if parent is None and self.tssb.parent() is None:
+            pass
+        else:
+            parent_param = jnp.zeros((self.n_genes,))
+            if parent is not None:
+                parent_param = parent.params[0]
+                
+            rng = np.random.default_rng(self.seed+2)
+            sampled_direction = rng.gamma(self.node_hyperparams['direction_shape'],
+                                            jnp.exp(-self.node_hyperparams['inheritance_strength'] * jnp.abs(parent_param)))
+            rng = np.random.default_rng(self.seed+3)
+            if np.all(parent_param == 0):
+                sampled_state = rng.normal(parent_param*0.1, np.exp(log_std)) # is root node, so avoid messing with main node attachments
+            else:
+                sampled_state = jnp.clip(rng.normal(parent_param*0.1, sampled_direction), a_min=-1, a_max=1) # to explore (without numerical explosions)
+            
+            self.variational_parameters["kernel"] = {
+                'direction': {'log_alpha': jnp.log(init_concentration*jnp.ones((self.n_genes,))), 'log_beta': jnp.log(init_concentration/self.node_hyperparams['direction_shape'] * jnp.ones((self.n_genes,)))},
+                'state': {'mean': jnp.array(sampled_state), 'log_std': jnp.array(rng.normal(log_std, 0.01, size=self.n_genes))}
+            }
+            self.params = [self.variational_parameters["kernel"]["state"]["mean"], 
+                        jnp.exp(self.variational_parameters["kernel"]["direction"]["log_alpha"]-self.variational_parameters["kernel"]["direction"]["log_beta"])]
+
+
     def reset_variational_noise_factors(self):    
         rng = np.random.default_rng(self.seed)
         n_factors = self.node_hyperparams['n_factors']