diff --git a/src/ensemble/distributions.py b/src/ensemble/distributions.py
index 750a556..37fdba6 100644
--- a/src/ensemble/distributions.py
+++ b/src/ensemble/distributions.py
@@ -42,27 +42,45 @@ def _create_scipy_dist(self) -> None:
 
 class InvGamma(Distribution):
     def _create_scipy_dist(self) -> None:
-        res = scipy.optimize.minimize(
+        optim_params = scipy.optimize.minimize(
             fun=self._shape_scale,
             # a *good* friend told me that this is a good initial guess and it works so far???
+            #   alpha = 3 is because alpha > 2 must be true due to variance formula
+            #   beta = mean * (alpha - 1) after isolating beta from formula for mean
             x0=[3, self.mean * 2],
             args=(self.mean, self.variance),
         )
-        print("results from minimizer: ", res.x)
-        shape, scale = np.abs(res.x)
+        shape, scale = np.abs(optim_params.x)
         self._scipy_dist = scipy.stats.invgamma(a=shape, scale=scale)
 
     def _shape_scale(self, x, samp_mean, samp_var) -> None:
         alpha = x[0]
         beta = x[1]
-        return ((beta / (alpha - 1)) - samp_mean) ** 2 + (
-            (beta**2 / ((alpha - 1) ** 2 * (alpha - 2))) - samp_var
-        ) ** 2
+        mean_guess = beta / (alpha - 1)
+        variance_guess = beta**2 / ((alpha - 1) ** 2 * (alpha - 2))
+        return (mean_guess - samp_mean) ** 2 + (variance_guess - samp_var) ** 2
 
 
 class Fisk(Distribution):
     def _create_scipy_dist(self):
-        raise NotImplementedError
+        optim_params = scipy.optimize.minimize(
+            fun=self._shape_scale,
+            x0=[2, self.mean * 2 / np.pi * np.sin(np.pi / 2)],
+            args=(self.mean, self.variance),
+        )
+        shape, scale = np.abs(optim_params.x)
+        print("parameters from optimizer: ", shape, scale)
+        self._scipy_dist = scipy.stats.fisk(c=shape, scale=scale)
+
+    def _shape_scale(self, x, samp_mean, samp_var) -> None:
+        alpha = x[0]
+        beta = x[1]
+        b = np.pi / beta
+        mean_guess = alpha * b / np.sin(b)
+        variance_guess = alpha**2 * (
+            (2 * b / np.sin(2 * b)) - b**2 / np.sin(b) ** 2
+        )
+        return (mean_guess - samp_mean) ** 2 + (variance_guess - samp_var) ** 2
 
 
 class GumbelR(Distribution):
diff --git a/tests/test_distributions.py b/tests/test_distributions.py
index 9a9911c..a679e4d 100644
--- a/tests/test_distributions.py
+++ b/tests/test_distributions.py
@@ -40,16 +40,19 @@ def test_gamma():
 
 
 def test_invgamma():
-    # raise NotImplementedError
     invgamma = InvGamma(MEAN, VARIANCE)
     res = invgamma.stats(moments="mv")
-    print("mean and var: ", res)
     assert np.isclose(res[0], MEAN)
     assert np.isclose(res[1], VARIANCE)
 
 
 def test_fisk():
-    raise NotImplementedError
+    fisk = Fisk(MEAN, VARIANCE)
+    res = fisk.stats(moments="mv")
+    print("est mean and var: ", res)
+    assert False
+    # assert np.isclose(res[0], MEAN)
+    # assert np.isclose(res[1], VARIANCE)
 
 
 def test_gumbel():