diff --git a/python/prophet/models.py b/python/prophet/models.py
index 1b74cee..3cf4b93 100644
--- a/python/prophet/models.py
+++ b/python/prophet/models.py
@@ -87,25 +87,28 @@ class CmdStanPyBackend(IStanBackend):
 
     def fit(self, stan_init, stan_data, **kwargs):
         (stan_init, stan_data) = self.prepare_data(stan_init, stan_data)
-        if 'algorithm' not in kwargs:
-            kwargs['algorithm'] = 'Newton' if stan_data['T'] < 100 else 'LBFGS'
-        iterations = int(1e4)
+        
+        if 'inits' not in kwargs and 'init' in kwargs:
+            kwargs['inits'] = self.prepare_data(kwargs['init'], stan_data)[0]
+
+        args = dict(
+            data=stan_data,
+            inits=stan_init,
+            algorithm='Newton' if stan_data['T'] < 100 else 'LBFGS',
+            iter=int(1e4),
+        )
+        args.update(kwargs)
+
         try:
-            self.stan_fit = self.model.optimize(data=stan_data,
-                                                inits=stan_init,
-                                                iter=iterations,
-                                                **kwargs)
+            self.stan_fit = self.model.optimize(**args)
         except RuntimeError as e:
             # Fall back on Newton
-            if self.newton_fallback and kwargs['algorithm'] != 'Newton':
+            if self.newton_fallback and args['algorithm'] != 'Newton':
                 logger.warning(
                     'Optimization terminated abnormally. Falling back to Newton.'
                 )
-                kwargs['algorithm'] = 'Newton'
-                self.stan_fit = self.model.optimize(data=stan_data,
-                                                    inits=stan_init,
-                                                    iter=iterations,
-                                                    **kwargs)
+                args['algorithm'] = 'Newton'
+                self.stan_fit = self.model.optimize(**args)
             else:
                 raise e
 
@@ -117,17 +120,26 @@ class CmdStanPyBackend(IStanBackend):
 
     def sampling(self, stan_init, stan_data, samples, **kwargs) -> dict:
         (stan_init, stan_data) = self.prepare_data(stan_init, stan_data)
+        
+        if 'inits' not in kwargs and 'init' in kwargs:
+            kwargs['inits'] = self.prepare_data(kwargs['init'], stan_data)[0]
+
+        args = dict(
+            data=stan_data,
+            inits=stan_init,
+            algorithm='Newton' if stan_data['T'] < 100 else 'LBFGS',
+        )
 
         if 'chains' not in kwargs:
             kwargs['chains'] = 4
         iter_half = samples // 2
+        kwargs['iter_sampling'] = iter_half
         if 'iter_warmup' not in kwargs:
             kwargs['iter_warmup'] = iter_half
+        
+        args.update(kwargs)
 
-        self.stan_fit = self.model.sample(data=stan_data,
-                                          inits=stan_init,
-                                          iter_sampling=iter_half,
-                                          **kwargs)
+        self.stan_fit = self.model.sample(**args)
         res = self.stan_fit.draws()
         (samples, c, columns) = res.shape
         res = res.reshape((samples * c, columns))
@@ -166,10 +178,10 @@ class CmdStanPyBackend(IStanBackend):
             'm': init['m'],
             'delta': init['delta'].tolist(),
             'beta': init['beta'].tolist(),
-            'sigma_obs': 1
+            'sigma_obs': init['sigma_obs']
         }
         return (cmdstanpy_init, cmdstanpy_data)
-
+    
     @staticmethod
     def stan_to_dict_numpy(column_names: Tuple[str, ...], data: 'np.array'):
         import numpy as np