diff --git a/scatrex/models/cna/node.py b/scatrex/models/cna/node.py
index fda885d..14c947f 100644
--- a/scatrex/models/cna/node.py
+++ b/scatrex/models/cna/node.py
@@ -12,8 +12,8 @@
 from ...utils.math_utils import *
 from ...ntssb.node import *
 
-MIN_ALPHA = jnp.log(0.01)
-MAX_BETA = jnp.log(1./0.001)
+MIN_ALPHA = jnp.log(1e-2)
+MAX_BETA = jnp.log(1e6)
 
 def update_params(params, params_gradient, step_size):
     new_params = []
diff --git a/scatrex/scatrex.py b/scatrex/scatrex.py
index 80ac1e5..42c7133 100644
--- a/scatrex/scatrex.py
+++ b/scatrex/scatrex.py
@@ -168,15 +168,15 @@ def learn_scales(self, n_epochs=100, mc_samples=10, step_size=0.01):
         n_cells = self.ntssb.data.shape[0]
         n_genes = self.ntssb.data.shape[1]
         gs = np.sqrt(np.mean(self.ntssb.data))
-
         root = self.ntssb.root['node'].root['node']
+        
         gene_scales_alpha_init = 10. * jnp.ones((n_genes,)) #* jnp.exp(np.random.normal(size=self.n_genes))
-        gene_scales_beta_init = 10. * jnp.ones((n_genes,))  * 1./gs * jnp.exp( 0. * np.random.normal(size=n_genes))
+        gene_scales_beta_init = 10. * jnp.ones((n_genes,)) * 1./np.mean(self.ntssb.data, axis=0) #* jnp.exp(10. + 0. * np.random.normal(size=n_genes))
         root.variational_parameters['global']['gene_scales']['log_alpha'] = jnp.log(gene_scales_alpha_init)
         root.variational_parameters['global']['gene_scales']['log_beta'] = jnp.log(gene_scales_beta_init)
 
         cell_scales_alpha_init = 10. * jnp.ones((n_cells,1)) #* jnp.exp(np.random.normal(size=[500,1]))
-        cell_scales_beta_init = 10. * jnp.ones((n_cells,1))  * 1./gs * jnp.exp( 0. * np.random.normal(size=[n_cells,1]))
+        cell_scales_beta_init = 10. * jnp.ones((n_cells,1)) * 1. #* jnp.exp(0. + 0. * np.random.normal(size=[n_cells,1]))
         root.variational_parameters['local']['cell_scales']['log_alpha'] = jnp.log(cell_scales_alpha_init)
         root.variational_parameters['local']['cell_scales']['log_beta'] = jnp.log(cell_scales_beta_init)