rejection sampling

Author: rasbt

README.md

@@ -29,7 +29,10 @@ If you have any suggestions or want to make additions, I would be very happy if
 
 - Linear regression via the least squares fit method [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/linregr_least_squares_fit.ipynb)] 
 
-- Dixon's Q test to identify outliers for small sample sizes [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb?create=1)] 
+- Dixon's Q test to identify outliers for small sample sizes [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb)]
+
+- Rejection sampling [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/rejection_sampling)] 
+ 
 
 
 

ipython_nbs/statistics/dixon_q_test.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:72ce647875bce795f311cf79fa5121b828fa08e8583099ccfada08c0b0c240dd"
+  "signature": "sha256:44d2aa8ef5d85aec0081655c5f05c38e8c1b6ee3879de1a46ad0476ef3f11c98"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,8 +19,18 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import time\n",
-      "print('Last updated: %s' %time.strftime('%d/%m/%Y'))"
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -v -d -u"
      ],
      "language": "python",
      "metadata": {},
@@ -29,11 +39,21 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 14/06/2014\n"
+        "Last updated: 18/07/2014 \n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.1.0\n"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
+     ]
     },
     {
      "cell_type": "markdown",
@@ -687,8 +707,6 @@
       "assert(dixon_test(test_data2) == [None, None]), 'expect [None, None]'\n",
       "assert(dixon_test(test_data2, q_dict=Q90) == [None, 0.542]), 'expect [None, 0.542]'\n",
       "\n",
-      "\n",
-      "\n",
       "print('ok')"
      ],
      "language": "python",
@@ -1109,7 +1127,6 @@
      "source": [
       "A more useful plot in my opinion is Tukey's boxplot<sup> 4</sup>. Boxplots are in facts one of my preferred approaches to quickly and visually indicate outliers in a Gaussian data set. However, also boxplots have to be used with real caution and might also not very informative for small sample sizes.\n",
       "\n",
-      "Q1 = Quartile 1\n",
       "\n",
       "\n",
       "<sup> 4</sup> Robert McGill, John W. Tukey and Wayne A. Larsen: \"[The American Statistician](http://www.jstor.org/discover/10.2307/2683468?uid=3739256&uid=2&uid=4&sid=21104147331297)\"\n",