diff --git a/pipt/update_schemes/update_methods_ns/margIS_update.py b/pipt/update_schemes/update_methods_ns/margIS_update.py
new file mode 100644
index 0000000..33e729f
--- /dev/null
+++ b/pipt/update_schemes/update_methods_ns/margIS_update.py
@@ -0,0 +1,45 @@
+import numpy as np
+from scipy.linalg import solve
+import copy as cp
+from pipt.misc_tools import analysis_tools as at
+
+class margIS_update():
+
+    """
+    Placeholder for private margIS method
+    """
+    def update(self):
+        if self.iteration == 1: # method requires some initiallization
+            self.aug_prior = cp.deepcopy(at.aug_state(self.prior_state, self.list_states))
+            self.mean_prior = self.aug_prior.mean(axis=1)
+            self.X = (self.aug_prior - np.dot(np.resize(self.mean_prior, (len(self.mean_prior), 1)),
+                                              np.ones((1, self.ne))))
+            self.W = np.eye(self.ne)
+            self.current_w = np.zeros((self.ne,))
+            self.E = np.dot(self.real_obs_data, self.proj)
+
+        M = len(self.real_obs_data)
+        Ytmp = solve(self.W, self.proj)
+        if len(self.scale_data.shape) == 1:
+            Y = np.dot(np.expand_dims(self.scale_data ** (-1), axis=1), np.ones((1, self.ne))) * \
+                np.dot(self.aug_pred_data, Ytmp)
+        else:
+            Y = solve(self.scale_data, np.dot(self.aug_pred_data, Ytmp))
+
+        pred_data_mean = np.mean(self.aug_pred_data, 1)
+        delta_d = (self.obs_data_vector - pred_data_mean)
+
+        if len(self.cov_data.shape) == 1:
+            S = np.dot(delta_d, (self.cov_data**(-1)) * delta_d)
+            Ratio = M / S
+            grad_lklhd = np.dot(Y.T * Ratio, (self.cov_data**(-1)) * delta_d)
+            grad_prior = (self.ne - 1) * self.current_w
+            self.C_w = (np.dot(Ratio * Y.T, np.dot(np.diag(self.cov_data ** (-1)), Y)) + (self.ne - 1) * np.eye(self.ne))
+        else:
+            S = np.dot(delta_d, solve(self.cov_data, delta_d))
+            Ratio = M / S
+            grad_lklhd = np.dot(Y.T * Ratio, solve(self.cov_data, delta_d))
+            grad_prior = (self.ne - 1) * self.current_w
+            self.C_w = (np.dot(Ratio * Y.T, solve(self.cov_data, Y)) + (self.ne - 1) * np.eye(self.ne))
+
+        self.sqrt_w_step = solve(self.C_w, grad_prior + grad_lklhd)
diff --git a/popt/update_schemes/enopt.py b/popt/update_schemes/enopt.py
index b786928..d71111e 100644
--- a/popt/update_schemes/enopt.py
+++ b/popt/update_schemes/enopt.py
@@ -29,6 +29,8 @@ class EnOpt(Optimize):
     References
     ----------
     Chen et al., 2009, 'Efficient Ensemble-Based Closed-Loop Production Optimization', SPE Journal, 14 (4): 634-645.
+
+    TODO: Implement getter for optimize_result
     """
 
     def __init__(self, fun, x, args, jac, hess, bounds=None, **options):