resolve issues from review

2 files changed, 7 insertions, 4 deletions

diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 6666d8628..482eabe14 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -111,7 +111,7 @@ def _hist_bin_scott(x, range):
     return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)
 
 
-def _hist_bin_ise(x, range):
+def _hist_bin_stone(x, range):
     """
     Histogram bin estimator based on minimizing the estimated integrated squared error (ISE).
 
@@ -119,6 +119,9 @@ def _hist_bin_ise(x, range):
     The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule.
     https://en.wikipedia.org/wiki/Histogram#Scott.27s_normal_reference_rule
 
+    This paper by Stone appears to be the origination of this rule.
+    http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf
+
     Parameters
     ----------
     x : array_like
@@ -258,7 +261,7 @@ def _hist_bin_auto(x, range):
         return sturges_bw
 
 # Private dict initialized at module load time
-_hist_bin_selectors = {'stone': _hist_bin_ise,
+_hist_bin_selectors = {'stone': _hist_bin_stone,
                        'auto': _hist_bin_auto,
                        'doane': _hist_bin_doane,
                        'fd': _hist_bin_fd,
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index 67a94877d..49c0d9720 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -544,8 +544,8 @@ class TestHistogramOptimBinNums(object):
             a, b = np.histogram(outlier_dataset, estimator)
             assert_equal(len(a), numbins)
 
-    def test_scott_vs_ise(self):
-        """Verify that Scott's rule and the ISE based method converges for normally distributed data"""
+    def test_scott_vs_stone(self):
+        """Verify that Scott's rule and Stone's rule converges for normally distributed data"""
 
         def nbins_ratio(seed, size):
             rng = np.random.RandomState(seed)