diff --git a/doc/pub/Bayesian/html/._Bayesian-bs000.html b/doc/pub/Bayesian/html/._Bayesian-bs000.html
index d02a7a85a..e8eb2961e 100644
--- a/doc/pub/Bayesian/html/._Bayesian-bs000.html
+++ b/doc/pub/Bayesian/html/._Bayesian-bs000.html
@@ -11,9 +11,9 @@ Automatically generated HTML file from DocOnce source
 <title>Data Analysis and Machine Learning: Elements of Bayesian theory</title>
 
 <!-- Bootstrap style: bootstrap -->
-<link href="http://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
+<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
 <!-- not necessary
-<link href="http://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
+<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
 -->
 
 <style type="text/css">
@@ -39,7 +39,8 @@ Automatically generated HTML file from DocOnce source
 </head>
 
 <!-- tocinfo
-{'highest level': 2, 'sections': [('Introduction', 2, None, '___sec0')]}
+{'highest level': 2,
+ 'sections': [('What is Bayesian Statistics', 2, None, '___sec0')]}
 end of tocinfo -->
 
 <body>
@@ -61,7 +62,7 @@ end of tocinfo -->
       <li class="dropdown">
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
-     <!-- navigation toc: --> <li><a href="._Bayesian-bs001.html#___sec0" style="font-size: 80%;">Introduction</a></li>
+     <!-- navigation toc: --> <li><a href="._Bayesian-bs001.html#___sec0" style="font-size: 80%;">What is Bayesian Statistics</a></li>
 
         </ul>
       </li>
@@ -96,7 +97,7 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 20, 2017</h4></center> <!-- date -->
+<center><h4>Dec 13, 2017</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -117,8 +118,8 @@ end of tocinfo -->
 
 </div>  <!-- end container -->
 <!-- include javascript, jQuery *first* -->
-<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
-<script src="http://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
+<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
+<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
 
 <!-- Bootstrap footer
 <footer>
diff --git a/doc/pub/Bayesian/html/._Bayesian-bs001.html b/doc/pub/Bayesian/html/._Bayesian-bs001.html
index e989b2996..803e4e261 100644
--- a/doc/pub/Bayesian/html/._Bayesian-bs001.html
+++ b/doc/pub/Bayesian/html/._Bayesian-bs001.html
@@ -11,9 +11,9 @@ Automatically generated HTML file from DocOnce source
 <title>Data Analysis and Machine Learning: Elements of Bayesian theory</title>
 
 <!-- Bootstrap style: bootstrap -->
-<link href="http://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
+<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
 <!-- not necessary
-<link href="http://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
+<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
 -->
 
 <style type="text/css">
@@ -39,7 +39,8 @@ Automatically generated HTML file from DocOnce source
 </head>
 
 <!-- tocinfo
-{'highest level': 2, 'sections': [('Introduction', 2, None, '___sec0')]}
+{'highest level': 2,
+ 'sections': [('What is Bayesian Statistics', 2, None, '___sec0')]}
 end of tocinfo -->
 
 <body>
@@ -61,7 +62,7 @@ end of tocinfo -->
       <li class="dropdown">
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
-     <!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">Introduction</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">What is Bayesian Statistics</a></li>
 
         </ul>
       </li>
@@ -77,12 +78,19 @@ end of tocinfo -->
 <a name="part0001"></a>
 <!-- !split -->
 
-<h2 id="___sec0" class="anchor">Introduction  </h2>
+<h2 id="___sec0" class="anchor">What is Bayesian Statistics  </h2>
 <div class="panel panel-default">
 <div class="panel-body">
 <p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
+Say something more general here. Reminder about probabilities from the statistics section
 
-<p>
+<ol>
+<li> Product rule</li>
+<li> Binomial distribution</li>
+<li> Gaussian PDF</li>
+<li> other PDFs</li>
+<li> Bayesian regression analysis</li>
+</ol>
 </div>
 </div>
 
@@ -98,8 +106,8 @@ end of tocinfo -->
 
 </div>  <!-- end container -->
 <!-- include javascript, jQuery *first* -->
-<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
-<script src="http://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
+<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
+<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
 
 <!-- Bootstrap footer
 <footer>
diff --git a/doc/pub/Bayesian/html/Bayesian-bs.html b/doc/pub/Bayesian/html/Bayesian-bs.html
index d02a7a85a..e8eb2961e 100644
--- a/doc/pub/Bayesian/html/Bayesian-bs.html
+++ b/doc/pub/Bayesian/html/Bayesian-bs.html
@@ -11,9 +11,9 @@ Automatically generated HTML file from DocOnce source
 <title>Data Analysis and Machine Learning: Elements of Bayesian theory</title>
 
 <!-- Bootstrap style: bootstrap -->
-<link href="http://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
+<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
 <!-- not necessary
-<link href="http://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
+<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
 -->
 
 <style type="text/css">
@@ -39,7 +39,8 @@ Automatically generated HTML file from DocOnce source
 </head>
 
 <!-- tocinfo
-{'highest level': 2, 'sections': [('Introduction', 2, None, '___sec0')]}
+{'highest level': 2,
+ 'sections': [('What is Bayesian Statistics', 2, None, '___sec0')]}
 end of tocinfo -->
 
 <body>
@@ -61,7 +62,7 @@ end of tocinfo -->
       <li class="dropdown">
         <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
         <ul class="dropdown-menu">
-     <!-- navigation toc: --> <li><a href="._Bayesian-bs001.html#___sec0" style="font-size: 80%;">Introduction</a></li>
+     <!-- navigation toc: --> <li><a href="._Bayesian-bs001.html#___sec0" style="font-size: 80%;">What is Bayesian Statistics</a></li>
 
         </ul>
       </li>
@@ -96,7 +97,7 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 20, 2017</h4></center> <!-- date -->
+<center><h4>Dec 13, 2017</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -117,8 +118,8 @@ end of tocinfo -->
 
 </div>  <!-- end container -->
 <!-- include javascript, jQuery *first* -->
-<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
-<script src="http://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
+<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
+<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
 
 <!-- Bootstrap footer
 <footer>
diff --git a/doc/pub/Bayesian/html/Bayesian-reveal.html b/doc/pub/Bayesian/html/Bayesian-reveal.html
index 251aea381..27b99f8d4 100644
--- a/doc/pub/Bayesian/html/Bayesian-reveal.html
+++ b/doc/pub/Bayesian/html/Bayesian-reveal.html
@@ -1,3 +1,4 @@
+\
 <!DOCTYPE html>
 
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
@@ -131,7 +132,7 @@ td.padding {
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Sep 20, 2017</h4></center> <!-- date -->
+<center><h4>Dec 13, 2017</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -142,10 +143,19 @@ td.padding {
 
 
 <section>
-<h2 id="___sec0">Introduction  </h2>
+<h2 id="___sec0">What is Bayesian Statistics  </h2>
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
+Say something more general here. Reminder about probabilities from the statistics section
+
+<ol>
+<p><li> Product rule</li>
+<p><li> Binomial distribution</li>
+<p><li> Gaussian PDF</li>
+<p><li> other PDFs</li>
+<p><li> Bayesian regression analysis</li>
+</ol>
 </div>
 </section>
 
diff --git a/doc/pub/Bayesian/html/Bayesian-solarized.html b/doc/pub/Bayesian/html/Bayesian-solarized.html
index 2ba916394..8b51f4470 100644
--- a/doc/pub/Bayesian/html/Bayesian-solarized.html
+++ b/doc/pub/Bayesian/html/Bayesian-solarized.html
@@ -15,7 +15,7 @@ Automatically generated HTML file from DocOnce source
 <script src="https://cdn.rawgit.com/hplgit/doconce/master/bundled/html_styles/style_solarized_box/js/highlight.pack.js"></script>
 <script>hljs.initHighlightingOnLoad();</script>
 
-<link href="http://thomasf.github.io/solarized-css/solarized-light.min.css" rel="stylesheet">
+<link href="https://thomasf.github.io/solarized-css/solarized-light.min.css" rel="stylesheet">
 <style type="text/css">
 h1 {color: #b58900;}  /* yellow */
 /* h1 {color: #cb4b16;}  orange */
@@ -59,7 +59,8 @@ div { text-align: justify; text-justify: inter-word; }
 </head>
 
 <!-- tocinfo
-{'highest level': 2, 'sections': [('Introduction', 2, None, '___sec0')]}
+{'highest level': 2,
+ 'sections': [('What is Bayesian Statistics', 2, None, '___sec0')]}
 end of tocinfo -->
 
 <body>
@@ -85,17 +86,24 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 20, 2017</h4></center> <!-- date -->
+<center><h4>Dec 13, 2017</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec0">Introduction  </h2>
+<h2 id="___sec0">What is Bayesian Statistics  </h2>
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
+Say something more general here. Reminder about probabilities from the statistics section
 
-
+<ol>
+<li> Product rule</li>
+<li> Binomial distribution</li>
+<li> Gaussian PDF</li>
+<li> other PDFs</li>
+<li> Bayesian regression analysis</li>
+</ol>
 </div>
 
 
diff --git a/doc/pub/Bayesian/html/Bayesian.html b/doc/pub/Bayesian/html/Bayesian.html
index dc88defae..1dd56fc73 100644
--- a/doc/pub/Bayesian/html/Bayesian.html
+++ b/doc/pub/Bayesian/html/Bayesian.html
@@ -64,7 +64,8 @@ div { text-align: justify; text-justify: inter-word; }
 </head>
 
 <!-- tocinfo
-{'highest level': 2, 'sections': [('Introduction', 2, None, '___sec0')]}
+{'highest level': 2,
+ 'sections': [('What is Bayesian Statistics', 2, None, '___sec0')]}
 end of tocinfo -->
 
 <body>
@@ -90,17 +91,24 @@ end of tocinfo -->
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Sep 20, 2017</h4></center> <!-- date -->
+<center><h4>Dec 13, 2017</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
-<h2 id="___sec0">Introduction  </h2>
+<h2 id="___sec0">What is Bayesian Statistics  </h2>
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
+Say something more general here. Reminder about probabilities from the statistics section
 
-
+<ol>
+<li> Product rule</li>
+<li> Binomial distribution</li>
+<li> Gaussian PDF</li>
+<li> other PDFs</li>
+<li> Bayesian regression analysis</li>
+</ol>
 </div>
 
 
diff --git a/doc/pub/Bayesian/ipynb/Bayesian.ipynb b/doc/pub/Bayesian/ipynb/Bayesian.ipynb
index d80560f36..d969f8c75 100644
--- a/doc/pub/Bayesian/ipynb/Bayesian.ipynb
+++ b/doc/pub/Bayesian/ipynb/Bayesian.ipynb
@@ -7,20 +7,31 @@
     "<!-- dom:TITLE: Data Analysis and Machine Learning: Elements of Bayesian theory -->\n",
     "# Data Analysis and Machine Learning: Elements of Bayesian theory\n",
     "<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
-    "<!-- Author: --> **Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
+    "<!-- Author: -->  \n",
+    "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Sep 20, 2017**\n",
+    "Date: **Dec 13, 2017**\n",
     "\n",
     "Copyright 1999-2017, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
     "\n",
     "\n",
     "\n",
-    "## Introduction"
+    "## What is Bayesian Statistics\n",
+    "Say something more general here. Reminder about probabilities from the statistics section\n",
+    "1. Product rule\n",
+    "\n",
+    "2. Binomial distribution\n",
+    "\n",
+    "3. Gaussian PDF\n",
+    "\n",
+    "4. other PDFs\n",
+    "\n",
+    "5. Bayesian regression analysis"
    ]
   }
  ],
  "metadata": {},
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
 }
diff --git a/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz b/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz
index ae533da38..afdbddbca 100644
Binary files a/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz and b/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz differ
diff --git a/doc/pub/Bayesian/pdf/Bayesian-beamer-handouts2x3.pdf b/doc/pub/Bayesian/pdf/Bayesian-beamer-handouts2x3.pdf
index c48f2a288..3f095066b 100644
Binary files a/doc/pub/Bayesian/pdf/Bayesian-beamer-handouts2x3.pdf and b/doc/pub/Bayesian/pdf/Bayesian-beamer-handouts2x3.pdf differ
diff --git a/doc/pub/Bayesian/pdf/Bayesian-beamer.pdf b/doc/pub/Bayesian/pdf/Bayesian-beamer.pdf
index be4fbfc9c..246f3cb3a 100644
Binary files a/doc/pub/Bayesian/pdf/Bayesian-beamer.pdf and b/doc/pub/Bayesian/pdf/Bayesian-beamer.pdf differ
diff --git a/doc/pub/Bayesian/pdf/Bayesian-minted.pdf b/doc/pub/Bayesian/pdf/Bayesian-minted.pdf
index de76540a0..0281d83a8 100644
Binary files a/doc/pub/Bayesian/pdf/Bayesian-minted.pdf and b/doc/pub/Bayesian/pdf/Bayesian-minted.pdf differ
diff --git a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
index 2a2857cfa..ad60515bc 100644
--- a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
+++ b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
@@ -629,11 +629,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
+      "  warnings.warn(mesg, RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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M7KzeOarp/j0xOM6RFT0d0CsYhQrueuhPA2uVUtOBfOAwcD+wQSk1Bdjg+D3gUAq6woqJ\nCotiWvI0r/dnMppIj0nnrMND1yGX4OZcDH1wkd5ft59Wc6tHwi1wTtDHhWcTZ4rTcfQQYUhBF5EE\n4DLgZQClVJdSqgG4AVjl2G0VcKO3jPQuik5jMbNTZo86s8BdsmKzqO+qdvSuCWbc9dALqgowipEL\nx1/okX7P9WdgVuosLeghgjse+mSgDvgfESkSkZdEJAZIV0o5A8HVQLq3jPQmFtWO2VjB3HTvh1uc\nZMZmctYh6BoN2MvlzkqZRXx4vEfO5xR0m1Lkp+Zz/OxxWs2tHjm3ZuzijqCHAfOA55RSc4FW+oRX\nlD2p2qWzKSIrRWS3iOyuq6sbrb0ep4USEOWT+LmTrLgsmsxnQMw65BLknCvONfA+jZ2NHDx90GPh\nFjgXclFKMTt1tp5gFCK4I+gVQIVSaofj97ewC3yNiIwHcPysdXWwUuoFpdQCpdSC1NRUT9jsUZo5\nBkrIT833WZ/OIl0G01ldbTHIcX5hGwZR9O2ntqNQo67f0hNnfzZlX8EI9MBoKDCkoCulqoFyEXGO\nGC4DPgNWA7c62m4F3vOKhV6mmaOE27KIMcX4rE9nWpqYzmoPPchxJ22xoKqAOFOcR1fJMvQIuSRE\nJDA5YbKOo4cAYW7u9/+AP4tIOFAC3Ib9y+ANEbkdOAnc7B0TvYfZZqaVE8RaPecZuUO3hx5e79N+\nNb5nqIlFSikKqgq4aPxFhBnc/TgOjfTw0AHyU/PZXL7ZvkKXlyfPafyHW2mLSqm9jrDJbKXUjUqp\ns0qpM0qpZUqpKUqpq5RSAadOR+uPYpNOIq3n+bTflKgUwiQcg6lee+hBzlBT/080naC6tXpEi0EP\nhtNDd8bw81PzaehsoKy5zKP9aMYWIT1T1FmQK9KW59N+RYTkiAzEVK9j6EHOUGmL26q2AbB4vGef\nErtj6A4X3Vm9UYddgpuQFvSi2iLCVQphyveTXJPDMzCEaw892Dm3wIVrRS+oKmBS/KTu5Qk9xbm0\nRfvPvIQ8Ykwx7KvVgh7MhKygK6UorCkkRp3nl+XgxkWOd4RctKIHNYP8f7usXeyq3uVx7xx6xtDt\n/RsNRmamzGT/aZ3pEsyErKCXN5dzpuMMMWqK28uDeZJx4RmIsZNWi15NJphRDBxu2XFqB+2Wdi7N\nutTj/Rpc9Jmfms/Rs0dpM7d5vD/N2CBkBb2otgiAGJt/PPTkiPEA1LRV+aF3ja9wLnHoivVl64kx\nxbBo/CKP92vo46GDXdBtysahM4c83p9mbBDSgh4fHk8E4/3S/7hIe7+17VrQg5mei5D3xGKzsKls\nE5dlXUa4Mdzj/Rr6pC0CzE7RA6PBTsgKemFtoWO6v/glLzclIgOA2g4t6MHMQB56UW0RZzvPcvWk\nq73Sb89aLk4SIxPJic/RA6NBTEgKen1HPScaTzA3be6gj8TeJCosBpslmto2vdBFMDNQDH3dyXVE\nGCM8stycK87VcundPjt1NvtP79eD8UFKSAr63tq9AOcE3U8T55Q5mTrtoQc1doeh9w1mUzY2lG1g\nyYQlRJuivdJv99R/W2/hzk/Np76jnormCq/0q/EvISvoJoOJGSkzuheJ9jUiYDMnU9uuPfRgRtH/\nEfDg6YPUttVy1aSrvNavqxg60F2Ebt9pHXYJRkJS0AtrC5mZMpMIY4TfbBAEW1cypzuqsdqsfrND\n411chfTWn1xPmISxNHup1/p1FUMHyEvMIyosSsfRg5SQE/QOSweHzhzqrn+uFP4JogsocxJWZaG2\nzWXlYU0QYC+G1fv39WXruWj8RR5bzMIV0qMeek/CDGHMStErGAUrISfoB08fxGKzMC9tHuAYtPKT\nLbauZAAqWnQ8M1jpG0M/evYo5c3lXg23ODFI/5ALnJtg1G5p97oNGt8ScoLunFA0J22OvcFPg6KC\nPYYOUNlS6XsDND6hb5bL+rL1GMTAFdlXeL1vg0i/kAvYM12sysqh03qCUbARkoKel5BHQkSCX+0Q\nEZQ5EcGgMw6CmL4x9PUn1zMvbR7josZ5vW+DiMtans7Ki7quS/DhuYr6AYBN2dhbu5drJ1/b3WbP\ncvHX95qR5Ig07aEHMT1nipY2lnK84Tj3X3j/EEd5BhHYX9HAqoLSftsSTeN5//NtGJuudHms0SB8\ncfZ4EqM9P4tV4z1CStCPNxyn2dzcHT8H/JaH7uwyNTJDe+hBTM/7a33ZegCWTVzmk74zE6PYevwM\nW4+f6bctckI69dGHeWD3wGGXTouN2y+Z7E0TNR4mpAS9qMYeP3dmuMDg1fC8ibPPlKgJHGnc5XsD\nND7DeXttOLmBWSmzyIjJ8Em/a+6+lNZO1ymxf/q8nBcP7uXT+xcTY4rtta3dbGXJ4xsxW22+MFPj\nQUJK0AtrC0mLSute0xMcaWX+mFjk6DMlYjxb2uvosHQQGRbpczs03sW5huepllMcPHOQu+fd7bO+\nI8KMRIQZXW6bmToVgLPmCrITZ/fa1t5l/xLQ1QECD7eCxyJSKiIHRGSviOx2tCWLyDoROeb46ftl\nf4bJ3tq9zEmbM6YWyU11VF2satElAIIR5xPghrINAD5JV3SHvET7sovFDcX9tjk/Hnp5xMBjOKOB\nVyil5iilFjh+vx/YoJSaAmxw/D5mqW6tpqq1innp83q1+z3k4hB0nYsenDizXNaXrWdK0hQmxU/y\nt0kAZMZmEm4Ip6SxxN+maDzIaNI7bgBWOd6vAm4cvTnew5l/3jN+Dv57rHR+h3QLuh4YDUoUCowt\nFNYUctXEseGdg33G6KSESS4FvdtD1w56wOGuoCvgIxHZIyIrHW3pSilnZalqIN3j1nmQwppCosOi\nmZo0tVe73UP3X5pLbFgSUWFR2kMPUpQCFXUQhRoz4RYneQl5rkMufps7rRkt7gr6JUqpecAK4N9E\n5LKeG5W9YITL73MRWSkiu0Vkd11d3eisHQVFtUXkp+YTZugzDuxvN0Tsj7+VzToXPRhRgC16PxPj\nJjIlcYq/zelFbmIuVS1VA64xqmumBx5uCbpSqtLxsxZ4B7gQqBGR8QCOny4rTCmlXlBKLVBKLUhN\nTfWM1cOkuauZo2eP9gu3OPFPba5zvWbGZmoPPUjptLWgIo+xbNKyMTUYD3YPXaEobSrt1a5DLoHL\nkIIuIjEiEud8D1wDHARWA7c6drsVeM9bRo6W/XX7USjmpvcXdH8PiqIgKy6LypZK7REFIbWWQhAb\nV0/0zlJzo2GgTJcet6YmwHAnDz0deMfhXYQBf1FKrRWRXcAbInI7cBK42Xtmjo7C2kKMYuxeJLcn\n/lqCrrt/7B56q7mVhs4GkiLHfPanZhicMu9CLInMSJnhb1P6MTFuImES1m9gdKw9SWjcZ0hBV0qV\nAPku2s8AvpnDPEqKaouYnjzd5XJfA63K7m169pgVmwXYqy5qQQ8e2sxt1Fn3Y+xYhEHGXh08k9HE\nxPiJA3vo2kUPOMbeXeaKUdxZZquZA3UHBoyf+4tzCxBAZpx95qpOXQwutlRuwYaZsPZ+/tCYIS8x\nz4WHbv+pJxYFHoEx9f+9u+D0Uci5xP7KvggiYoc+Djhcf5gOa0e/CUVO/BVy6fmhcXroemA0uFh/\ncj3hxGM05/rblAHJTchlQ9kGOq2d/ZZk1B564BEYgp52Ppw5BgXPwJbfgCEMJsxzS+ALawqB/hOK\nnPir2mLP/qNN0SRHJusyukFEp7WTjys+JtV4IU1j+EE4LzEPm7JR2ljKtORpQI+nR38aphkRgSHo\nF99lf3W1QvkOKN1if7kh8JsrNjM1aSopUSkuT22/af0bQwdH6qIOuQQNO07toM3SxvTwhTSP4UHG\n3AT700NJY0m3oHejXfSAIzAE3Ul4DORdaX/BkAJfn72QoppCVs64bcBT9l3E11ecC7nYyYrN4uCZ\ng743ROMV1p1cR5wpjiTDBUCTv80ZkJyEHAxi6D8wOna/gzSDEFiC3pchBP7jg69iS0niynWPw94P\nRhSD9x69V2XPjMtk3cl1WG1WjAbXJU81gYHFZmFT+SaWZi+lsypsTItjhDGC7Ljs/gOj6JBLIBLY\ngt6XPgK/cf33GX/6INMX3gQnt7oM0cwzJ9Bsm+83k3t66BZloaathgmxE/xmj2b07K7ZTWNnI1dN\nvIq/V419bzc3IdeFhy464hKABJeg96DN3Ma26p3cNPUmxLmGo4sQzS9tFqytRnhpvk89+L4f8p6p\ni1rQA5v1J9cTFRbFxZkX8776fMwXu8pLzOPTik8xW82YjKbudp22GHgEraBvq9pGp7WTK7N7LILr\nIkRz/+9eZGn4UVbI8T4e/NweAr/I4wLf/RF3fGZ6Ti7SBC42ZWNj2UYuybyEqLAoFGAY23pObkIu\nFmWhrLmsuxyAoMdEA5GgFfSN5RuJD48fMP8cgPAY9oTNpWHcpaz41nwXHvx/w5bfekXgz6WG2T81\nGTEZGMVIeXP5qM6r8S/76/ZT117XvRC0PS12bCu6U8RLGkvOCbroGHogEpSCbrFZ2Fy+mcuzL+9f\nLtcF3Z+3IbNoPC/wTi8ozBBGRkyG9tADnPUn1xNmCOOyLHuFaZtSYzzgAjnxOYC9SNfVk+xFxMZ6\nmEjjmqAU9MKaQpq6mnqHWwZg0GqLXhR4V11mxWbp2aIBjFKK9WXrWTx+MXHhcfY28G/1NzeINkWT\nGZtJSUOPTBfRIZdAJCgFfWP5RiKMESyesHjIfZVS7nsjHhR4VzWns+Ky2Fy+2T1bNGOOz+s/p7Kl\nkpWzV55r9HM1T3fJTciluPFcpos9bVEreqARdIKulGJj2UYWT1jssrpiv/1h5J84dwVejJA5r7fA\n9+zfQWZsJmc6ztBmbnPLds3YYn3Zegxi4IrsK7rb/FXNc7jkJeax49QOLDbLuTCl1vOAI+gE/fP6\nzznVeop/zf9X9w7w5E07DIE/LzWfn4RlMa66HfKWQ0QsWXH2TJeqlirOSzrPg4ZpfMGGkxtYkL6g\nVwlkf9fbd5fchFy6bF1UtlQyKX6SHhQNUIJO0DeWb8QgBpZmL3X7GK994AYReHV0M981/gPTJ6vh\nU7sHnznhAgAqzh7Tgh5glDSWUNxYzM3Teq/z4u/ib+7Sc/WiSfGTEESvoBWAjN0ycCNkY9lG5qbN\nJTky2a397YOiPvrEOQV+2c85cf07zO58kV2X/Q9c8iMQI5mFfwWg8t2V8NJVsP5BOL4eOlt8Y59m\nxGw4uQGgO13RiWIYYzR+pGeRLgiMLyFNf4LKQy9vLufo2aPct+A+t49RfkwrayeSM2nzYeaXAUju\nbCHqjaVUTFoIDa2Dx+D9XotG05N1J9cxO3U26THpvdoDxUOPDY8lPTq9uwSAnlgUmASVoG8q2wTA\nFROvGGLPc/jrnnX1IZeIWDLjJ1IRlwlf+W9HiGane4OsWuD9RmVLJYfrD3PP/Hv6bQskTcxLzDsn\n6CIBZbvGjtuCLiJGYDdQqZT6oohMBl4DxgF7gG8ppbq8Y6Z7bCrfxNSkqWTHZQ/rOL+Uzx1gbfWs\nuKxzk4vCYyDvCvsLtMCPUZzhlqsmXtVvWyDMFHWSm5DLWzVvYVM27aEHKMPx0H8IHAbiHb8/AfxW\nKfWaiDwP3A4852H73OZsx1kKawt75wC7gd+XoOvzocmKzWLHqR2OOu19LNMCPyZZX7aeaUnTyI53\n5UiM/ZmiTvIS8+iwdlDVUgXoPPRAxC1BF5Es4AvAo8A9YleaK4FvOHZZBTyIHwX944qPsSmbW7ND\ne+LvPOG+H5msuCzaLe2c7Tw79MDukAL/ey3wHqDsTBsffVbtclurpZ6i2r0sSvoaL31a0m/7yTNt\nhIcFRu5Bz5oueqZoYOKuh/474CdAnOP3cUCDUsri+L0CyPSwbcNiY9lGxseMZ3ry9GEd528PvS+Z\nsefK6LqbqdONFniv8NzHxfx1Z5nLbabkT4lMV2zYncG6rsMu97l2RrrL9rGGM9OluKEYQZdwDkSG\nFHQR+SJQq5TaIyKXD7cDEVkJrASYOHHisA10h3ZLO9uqtvGVqV8ZtrftLy/EGUN3FXIB+0Db7NTZ\no+tkOALfXargUph4EUTEDX7uEKLLYmN8QiQf/uiyXu1KKb7+wbMkRc7huX/+1oDHx4YHRu5BQkQC\nKVEpdkEXv/pnmhHizp22BLheRK4DIrHH0J8GEkUkzOGlZwEuywQqpV4AXgBYsGCBV+SzoKqADmvH\nsMMt3fgxyNk3Tulc3MIrC0aUdkJQAAAgAElEQVQPJfDb/gBbf6cFvg9KKYwGIT7S1Kt9x6kdlLeU\n8a9z7uy3LVDJS8ijpLEEkcv0xKIAZEhBV0r9B/AfAA4P/V6l1DdF5E3gJuyZLrcC73nRzkHZWOZG\n7fNB8MfEj4EeJKJN0YyLHOebMrpa4N3CphQGF/+w14+8TkJEAtfkXOMHq7xDbmIuq4tXo4dEA5PR\nPAv+O/CaiDwCFAEve8ak/rz0aQmHTzW73GZTVj7u2EiKcQ7//tahYZ+7vrXLT2mLdlw5QZlxmd7x\n0IdCC7xLbKr/qkN1bXVsKtvEN8//JhHGCP8Y5gXyEvJoNbdiNDailA67BBrDEnSl1GZgs+N9CXCh\n503qz+fVzWwvOeNymyX8GOZxLTTUTWV7p+t9BiM5JpwLc4Y5+OgButMWXWzLis1iX90+n9rjEi3w\ngGsP/Z3j72BRFr467at+sso75CbaB0YJr0Vxvn+N0QybgBiteeqr+QNue2LnLt48GsHmu74XkCVn\nXcUpM2Mz+bD0w96lTMcCISrwfafvW21W3jr6FovGL2JS/CT/GeYFnJkumFynaWrGNmNILYZPd+3z\n8e7VPh9bDBznyY7LxqqsVLdWd5fUHZOEiMD39dC3VG7hVOsp7lvofs2gQCE5MpnEiESaTDU6Dz0A\nCWhBP3L2CFWtVdyZf6e/TRk2g8Xtu3PRWyrGtqD3JUgFXil6CfobR98gJSqFy7Mv959RXkJEyE3I\npaipRg+KBiABLegby4Zf+3ysMNSgKEBlcyWM951NHidIBN6mVPcXcFVLFZ9WfMp3Z38XkyE4UhX7\nkpeYR2H1P7DZtKQHGgEv6HNS5wx/RuUYor61i6qG9l5tNls8RjHy+elSqtLaXR4XaTKSHBPuCxM9\nR4AKvK2Hh/7W0bcQEW6acpPf7PE2eYl5YGijUzX42xTNMAlYQa9oruDI2SPcu+Bef5syIiJMRgAe\n/vtnPPz3z/ptj8lL5P8Kd/LiatelDETg/bsuYWZmglft9CoBIvBKKQwGMNvM/O3Y37gs8zLGxwby\no9PgOAdGm60+mAuh8SgBK+ibyu21z0c8O9TPZCZG8eK3F1Df2uly+5pTF7G/YQM//dIUwg2Rvbad\nPNPGs5uLOdPq12rFnmeMCrxzUHRj2UbOdJwJulTFvjiLdDXbtKAHGgEr6BvLNjIlacoAJUsDg6sv\nGLhoU171V7ntww9ISStmxeQVvbYVlZ3l2c3F2II9DaGfwLdBhe8F3uaoaf7mkTeZEDOBJROWeOzc\nY5HUqFSwRdKiBT3gCEhBd9Y+/+6s7/rbFK8xL30eaVFprDmxpp+gGwablRTMhEdD7uX2F/hM4G1K\nYTFUs6N6Bz+c90OMBqMH/pixi4hgsGTQEuaH2cqaURGQgt5d+3xiYIZb3MEgBq7JuYbXj7xOc1cz\nceHnBMmp50HvoQ+FjwReKWg2bSFMwrjxvBu98IeMPYzmDFrC+4/taMY2ASnoG8s2khGTwfnJwT01\necXkFfzp8J/YWLaRG867obvd6aHrrLI+eEngLaqTRlMBV09aRkpUig/+EP9jsKTTyXbqO+oDOoss\n1Ag4QXfWPv/ylC8HzFqNI2VWyiwyYzNZW7q2l6CfW75OK/qgDEvg5/QQ+EW9BL5BdmOTNm6eerMf\n/gj/YLTas3hKGkpIztCCHigEnKB31z4P4nCLExHh2pxrefXQq5ztOEtSZJK9He2hj4ghBf5Z2Pp0\nP4FvMGwi3JbOwoyF/rPdxxjM9gH7ksYSFmQs8LM1GncJjMUOezDa2ueBxorJK7AoC+vL1ne3GRz/\nNe2hjxKnwF/5X/CdtXB/GXz7Pbj0HjCGw7ZnOfLm12k1lnJLUz2y/gE4tg46XZdyDiYMKgkjkfb1\nRTUBQ0B56BabhY8rPmZp1tKgnXbdl2lJ08iJz+HDEx/y1an2/Ofu5ev8aVgw4sKDf3PzjwmrKuCS\ntogBPfi+IZpgwCBCjEyguKHY36ZohkFACXpRbRGNnY0hEW5xIiIsn7ycP+77I3VtdaRGp3YvthDy\nWS5eplUU79cVEmW5kGfS7uD/vjXLrRBNMAi8YBf0koaj/jZFMwwCStA3lm0kwhjBxRMu9rcpPmV5\nznKe3/c8H538iG+e/83uwWCt597lHyX/oM3SxgTzZUiEjDgGH6gCHyOZHG/fQlNXE/Hh8f42R+MG\nASPogV37fHTkJeYxJWkKH5Z+6BB0e7v20L2HUoo3jrzB9OTpdDTn9FuCDghqgReBaLEvWF7SUMKc\ntDl+tkjjDgEj6IFc+9wTrMhZwTNFz1DdWo1B7GKg9dx7HDh9gCNnj/CzRT/j1VJcLhLdj5EKfPZF\nEDm2PGChh6A3akEPFAJG0AO59rknWJ6znGeKnuHD0g9Zmm4v3arXZfcerx95neiwaL6Q+wX+11bo\n2kMfigAWeBEhklQijBF6YDSAGFLQRSQS+ASIcOz/llLqARGZDLwGjAP2AN9SSnmt/F8w1D4fDdnx\n2cwYN4M1J9ZwRYY928Vm87NRQUpjZyMfln7IDXk3EGOKcSxw4YFJbAEk8AIgBiYnTKa4UQt6oOCO\nh94JXKmUahERE7BFRNYA9wC/VUq9JiLPA7cDz3nDyECvfe4pVkxewVO7n+JUWzmgY+jeYnXxajqt\nndw87dzM0BF56EMxlgVe7OMIuQm5FNUWebcvjccYUtCVffZKi+NXk+OlgCuBbzjaVwEP4iVBD/Ta\n557i2pxreWr3U3xatQGYpAMuXsA5GJqfms+05GlA/0WivcYYE3iFfUD+gxMf0GpuJcYU4/E+NJ7F\nrRi6iBixh1XOA/4AFAMNSimLY5cKIHOAY1cCKwEmTpw4IiODofa5J8iIyWBu2lw+qVoH3KFninqB\n3TW7KW0q5dFLHu1usyk3B0U9jR8FXgAU5CXYF7s40XiCmSkzR3VOjfdxS9CVUlZgjogkAu8ArtdF\nc33sC8ALAAsWLBiRAj1x2RPUttWO5NCgY3nOch7b+RiG8BqUmuVvc4KO14+8Tnx4PNdMuqa7reci\n0X7FhwIvIigUuYn25eiKG4q1oAcAw8pyUUo1iMgmYDGQKCJhDi89C/Da8iZp0WmkRad56/QBxTU5\n1/DEzicIi9+PTV3lb3OCitPtp9lwcgNfP//rRIadW/ZP+ctDHwovCrzzr82OyybMEKYHRgMEd7Jc\nUgGzQ8yjgKuBJ4BNwE3YM11uBd7zpqEaOylRKeSnzmNP5z6sOs3Fo7x7/F0sytJdM8eJPYbuJ6OG\ng0uB3+VC4A0wvs9Epz4CL2L/IgszhJETn0NJgy7SFQi446GPB1Y54ugG4A2l1N9F5DPgNRF5BCgC\nXvainZoeXJF1LUV1j1LTeQLI8bc5QYHVZuWto29xUcZFTE6Y3GubzwZFPU14NOQutb+gv8Bvfw4K\nnnEp8IJ0T1zLS8zj0OlD/vs7NG7jTpbLfmCui/YS4EJvGKUZnKWZV/Drwsc43PQxcIW/zQkKtlZt\npbKlkh/N/1G/bTYbwbGYyjAE/hlDHqUyF45+lbyYLD4q/YgOS0evUJRm7BEwM0U150iMTMLaOoXP\nTZ+iPDXpJcR588ibjIsc5zI1VgVKyGW4DCLwXVv/wbKGv8Ff3iA3JgaVNo7SD+9j+pR/chmi0YwN\ntKAHIAYBc9NsmmLf5MDpA8xOne1vkwKaUy2n+KTyE26feTsmY/86+zbF2Mhy8TY9BP6+/ZeSl2jg\nD5dZyTv2D6heQ/Hhd5i+61W3YvAa/6AFPQARESzNMzDyDmtOrNGCPgKsNit76/ayoWwD60+uRynF\nTVNvcrlvwMbQR4GI0GWIhNwFTJp0McY/f0Tx5fdA8ny3YvBa4P2DFvQARASwRZITPZ+PSj/ivoX3\nYZCAW03Q53RZu9h+ajsbyzayqXwT9R31mAwmFo1fxP0X3s+E2Akuj7N76CEm6Jyr5mkympgYP5GS\n5jJYcM+IBlm1wPsGLegBiNNbnBJzCWvrdlBYUxj0C/kqpXjqoyOU17cP6ziLaqfetp86tZsztn1Y\n6cBIJOMM+VxgnM84Qz5hdVG8WwfvbnVds6SpwxycMfRBEOdUUQd5CXkcbzjee6dRZNFogfcOWtAD\nEKe4TIpaSFRYFGtL1wa9oDd3WvjDpmKSY8JJiBp8PVmbNNMVcZDO8H2Yw4+AWBBbLOGdc4npysfU\nNRUbJmqBWrqAwYuEZiVGsSh3nOf+mAChZ2WJyQmT2VS+iS5rF+HGcNcHaIH3O1rQAxDnItFhhkiW\nZi1l3cl13H/h/YQZgvffabbYJ1HdfdUUvr04p9/2urY61pauZUPZBopqi7ApG5mxmVw58essm7iM\nOalzMBqMPrY6cBHpvQh5XmIeVmXlZNNJpiRNce8kWuB9TvAqQBDTcwm65ZOXs7Z0LTurdwb1Wqtm\nq11eTMbeYwVWm5XXjrzGM4XP0GZpY2rSVFbOXsmyicuYljQt5GLfnsI+sahHyCXRXqSruLHYfUHv\nixZ4r6MFPQBxapRScEnWJcSYYlh7Ym2QC7rdQ+8p6Efqj/BgwYMcPHOQSzIv4b6F95GbkOsvE4OK\nvt+DOfE5COLZEgBBJPBNHWYOVDS63Ga2dXGseTd3zPsnrzsYWtADEOegqFKKCGMEV2Zfyfqy9fxs\n0c9c5lEHA13dgi60W9p5ft/zrDq0ioSIBJ687EmW5yzX3rgHEXqHXCLDIsmKy/LucnQBLPC//Mdh\nXttV7nJbeMp6IlLXc9HEHK+nGGtBD0Ccgm5zfOKWT17O+yXvU1BVELRrrnY5YuilrUV8+b0/UNFS\nwZenfJl75t9DQkSCn60LQkT6LUKel5BHSaMPi3QFkMA3d1jITIzit//cezHtU20neXDPxyxIvdIn\n80W0oAcgTj/U+YFbPH4xCREJrC1dG7SCfqb9DJHjX+el40XkxOfwyrWvsDBjob/NCmr6Ll6Qm5jL\nlqotmG1mTAY/PAmOYYG32hQxEUYunHxuzWObsnHb2h8RZYrksct/5tX+nWhBD0B6DoqCfeLHVROv\nYs2JNUFXQEkpxfsl7/PY9icJS2hheea3eOSKHxJhjPC3aUGNfWJRb0nPS8zDYrNQ3lw+NsYqhhL4\nHc/3EPj8PgLv2ac6q4vZxO8ce4fC2kIeXPwgKVEpHu1vILSgByDSI4buZPnk5bx97G0+rfyUqydd\n7S/TPEpZUxkPb3+YHad2kBc3k5ojV/OlS6/XYu4DXA1HOJejK2koGRuC3pchBf6PUPDfXhF4m01h\n7DH77HT7aX6959fMT5/Pl6Z8aVTnHg5a0AMUQ5884YXpCxkXOY61J9YGvKCbbWZWHVrF8/uex2Qw\n8bNFPyONpfzLzt2EG3WJA1/ganjZWSe+uKGYqyYFwGpZPhR4q+ot6E/ufJIOSwc/X/xzn5bl0IIe\noBhEukMuAEaDkasnXc27x9+lzdxGtCnaj9aNnP11+3lw24McO3uMqyddzf0X3k9adBobDtcA/fPQ\nNd5BXAyKRpuimRAzIXCXo/OiwFtt50Iun1R8wprSNXx/zvd9/iSjBT1AETmX5eJkxeQVvHbkNTaX\nb+a63Ov8Y5ibdFg6qG2rpbq1muq2ampaazjecJw1J9aQGp3K01c8zZUTz9Umd5WHrvEe9rTF/mu6\n5ybmcqLxhO8N8gYeFHibw0NvM7fx6PZHyU3I5faZt/v8T9KCHqC48qDmpM0hLTqNNaVrvCboe07W\n88TaI9j6fpv0wIYZizRgkXrMchaL42WWs1gM9vdWael3nFHFkGC5nPjTN/DsPyJ5loLubfWt9nor\n4WE619xX9L2/wB5H31W9C6vNGnylFPoKvLndbYG32hRGEf6w9w9UtVaxavmqgWveeBEt6AGKqywE\ngxhYnrOcv3z+F5q6mogP93yq1uYjdew8Uc+S83oXq1LYqJet1Br/jlnq+x1nVDGYSCJcJRFjm4yJ\nZEwqiXCSMKkkTCRhwDHY6SIjbnxiJBdMiCc7OTBDSYGGc5HovuQl5tFp7aSqpYrs+GzfG+ZLTFEw\n+TL7CwYV+EeMeayPyeO5zw5zU+71zEuf5xeThxR0EckGXgXSsY/DvaCUelpEkoHXsa9SXArcrJQ6\n6z1TNT3pG0N3smLyCl797FU2lm3kxvNu9Hi/rZ1WYiPC+PMdi7rbjtQf4Rfbf0Fl3T7mps1lyYRv\nkB6TTkZMBunR6aRHpwdsTD9UEWTAkAvYa7oEvaD3ZRCBbyv4Oxti9pFsMfCjzc/B4a1eTZMcCHc8\ndAvwY6VUoYjEAXtEZB3wL8AGpdTjInI/cD/w794zVdMTwwAe1IxxM8iKzeL1z19n0fhFZMRkeLTf\ndrOF6HD7o3abuY1n9z7Lnw7/ifjweB5Z8gjX512vp+AHAwP8C52DfMUNxVyefbnv7BmL9BD4Hxwz\ncybiLZ46/3bic9v6h2gmLYFb3/f6WoZDCrpS6hRwyvG+WUQOA5nADcDljt1WAZvRgu4zRKTfoKiz\n/fZZt/PI9kdY8fYKrsu9jttm3MZ5Sed5pN/WTitR4QbWn1zP4zsfp6athpum3sTd8+7WU/CDiJ4r\nFvUkLjyOtOg035YAGONUtlRSH76aeJXPNQt/eE60e4ZozO0+WZh2WDF0EckB5gI7gHSH2ANUYw/J\naHyEvV6164HJm6bexMUTLubVz17lb8f+xuri1SzNWsp3Zn5n1LG9+q5TtCW9yo82H2Jq0lSeWvoU\nc9LmDH2gJqBwlUXlJDch17tFugIIpRS/2P4LQMjhlt5Pp31DND7AbUEXkVjgbeBupVRTT8OVUkpE\nXP77RWQlsBJg4sSJo7NW041gLwh0qtH1kmxCErdO+yFfmnwr75W8yXslb3Dr2luZkZzPzVO+zUUZ\nS4Y14cFsM/PWsT+xX72EhBm4b8F9fOP8bwT1ohqhTnl9G4+tOdyvvb45iZKOIn75wSFkgHtoxczx\nzMlO9LaJfmdt6Vq2Vm4lof0rREX7Znr/YLj1aRQRE3Yx/7NS6m+O5hoRGa+UOiUi44FaV8cqpV4A\nXgBYsGDBwLlummERaTLy1p4K3tpT4cbeeSA/xpS4iwNdn3Ko/sdYO9PoOrMUS2M+Q90GxuhiIjLe\nxRhRh7l5Jgvj/oVvz7jWI3+HZmwyc0ICRWUN/O/W0n7bJHYcpvGd/N/BN7E1XdRve6fFRtmZNp67\nZb4PLPUfjZ2NPL7zcWaMm8GZ+qUYY/1tkXtZLgK8DBxWSv2mx6bVwK3A446f73nFQo1LnrtlPsdq\nmod51Hys6g4ON33KtjNvURvxJnETN3FR8o3MTVpOuCGq194tlrNsqHmZg02bSDSlc23GQ5wXu4CL\nQnB9zVDjv754Af/1xQtcblNqObd/dITDpg9594Z/JT2md7R1xdOfYhlknkKw8Ns9v6Wxs5Hnr3qe\nu47U9Jr67y/c8dCXAN8CDojIXkfbf2IX8jdE5HbgJHCzd0zUuGL+pCTmT0oa4dG5KPVttlRu4ZWD\nr7C+5iV2NrzB16Z/jW+e/00SIxJ56+hbvFL4O9ot7aycvZLvzvpuUFVx1IwcEeHBxQ/y5dVf5tEd\nj/L0FU/3ih0bhEEnngUDu6p38faxt7ltxm2cP+58bKqmX7VFf+BOlssWBkxiYplnzdH4ChHh0qxL\nuTTrUvbX7eeVg6/w4v4XWXVoFVmxWRQ3FnNhxoX8dNFPx2ZlPY1fmRg/kbvm3MWv9/yaj05+xLU5\n50JwRoNgdZUiEyR0Wjt5eNvDZMZmcmf+ncC5qf/+RhfG0DA7dTa/u+J3vHvju3wh9wsYDUYeu/Qx\nXrrmJS3mmgG55YJbmDFuBr/c8UsaOhq62w0iWIPYQ3/pwEuUNpXys0U/654w55z672+0oGu6yU3I\n5aGLH+Lt69/mi7lf1BOENIMSZgjjoYsfoqmziV/t/lV3u9HQv85QsFDcUMxLB17iusnXsSRzSXe7\nzaYwaA9do9EEMtOSp/GdWd9hdfFqtlRuAewx9GD00G3KxkPbHiI6LJqfLPxJr21WpT10jUYTBHxv\n9veYnDCZh7c9TKu51R5yCUIX/e1jb1NUW8S9C+5lXFTvTC+rDe2hazSawCfcGM7DFz9MdWs1zxQ+\n4wi5BJeg17TW8Nvdv2VhxkKXRe/sg6J+MKwPepqfRqMZNXPS5vD16V/nr5//lTxDNmKe7G+TRk1F\ncwUfV3zMJxWfsKt6F4Lw80U/dzm2NFYGRbWgazQaj/DDeT9kU/kmylteZbz6T3+bM2ysNiv76vZ1\ni/jxhuMA5MTn8I3p3+ALuV8gJyHH5bFjZVBUC7pGo/EI0aZoHlj8AHeuv5Ozpg84V4zVN3RarNzw\n+63UNHW4fYySdlTU56iow6iow2BsA2VAOnIxtF+PtF9AxclU/nwA/kw5UO7yPM2dFsK0oGs0mmBi\nSeYSUljC6fAPOVJ/O9OSp/ms74Y2M59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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10acf4518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -720,7 +739,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }
diff --git a/doc/pub/How2ReadData/ipynb/Untitled.ipynb b/doc/pub/How2ReadData/ipynb/Untitled.ipynb
new file mode 100644
index 000000000..99b4411d5
--- /dev/null
+++ b/doc/pub/How2ReadData/ipynb/Untitled.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'data.frame':\t24 obs. of  2 variables:\n",
+      " $ x: num  0.838 0.5285 0.8344 0.0721 0.9242 ...\n",
+      " $ y: num  0.5448 0.1412 0.6284 0.0106 0.7139 ...\n",
+      "1.814289 :  1\n",
+      "0.3012573 :  2.050361\n",
+      "0.08280959 :  2.881157\n",
+      "0.07643193 :  3.091093\n",
+      "0.07643191 :  3.091473\n",
+      "0.07643191 :  3.091458\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "'nls'"
+      ],
+      "text/latex": [
+       "'nls'"
+      ],
+      "text/markdown": [
+       "'nls'"
+      ],
+      "text/plain": [
+       "[1] \"nls\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Formula: y ~ I(x^power)\n",
+       "\n",
+       "Parameters:\n",
+       "      Estimate Std. Error t value Pr(>|t|)    \n",
+       "power    3.091      0.161    19.2 1.17e-15 ***\n",
+       "---\n",
+       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
+       "\n",
+       "Residual standard error: 0.05765 on 23 degrees of freedom\n",
+       "\n",
+       "Number of iterations to convergence: 5 \n",
+       "Achieved convergence tolerance: 8.046e-07\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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m7ZOFees5kA1ac8s1Hnm/XKmJ7WWJ/7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",
+      "text/plain": [
+       "Plot with title “Known cubic with noise”"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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BBBBAAAEEqiTQ5pp3bJux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\nQAABBBBAAAEERiLw/7fHz41dOr0eAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "Plot with title “Fitted power model”"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "set.seed(1485)\n",
+    "    len = 24\n",
+    "    x = runif(len)\n",
+    "    y = x^3+rnorm(len, 0,0.06)\n",
+    "    ds = data.frame(x = x, y = y)\n",
+    "    str(ds)\n",
+    "    plot( y ~ x, main =\"Known cubic with noise\")\n",
+    "    s  = seq(0,1,length =100)\n",
+    "    lines(s, s^3, lty =2, col =\"green\")\n",
+    "    m = nls(y ~ I(x^power), data = ds, start = list(power=1), trace = T)\n",
+    "    class(m)\n",
+    "    summary(m)\n",
+    "    power = round(summary(m)$coefficients[1], 3)\n",
+    "        power.se = round(summary(m)$coefficients[2], 3)\n",
+    "    plot(y ~ x, main = \"Fitted power model\", sub = \"Blue: fit; green: known\")\n",
+    "    s = seq(0, 1, length = 100)\n",
+    "    lines(s, s^3, lty = 2, col = \"green\")\n",
+    "    lines(s, predict(m, list(x = s)), lty = 1, col = \"blue\")\n",
+    "    text(0, 0.5, paste(\"y =x^ (\", power, \" +/- \", power.se, \")\", sep = \"\"), pos = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/src/Bayesian/Bayesian.do.txt b/doc/src/Bayesian/Bayesian.do.txt
index 0c3987f67..bb849e14f 100644
--- a/doc/src/Bayesian/Bayesian.do.txt
+++ b/doc/src/Bayesian/Bayesian.do.txt
@@ -6,34 +6,11 @@ DATE: today
 !split
 ===== What is Bayesian Statistics  =====
 !bblock
-Say something general here. Reminder about probabilities from the statistics section
+Say something more general here. Reminder about probabilities from the statistics section
 o Product rule
 o Binomial distribution
 o Gaussian PDF
 o other PDFs
-!eblock
-
-!split
-===== Bayesian regression analysis =====
-!bblock
-!bt
-\[
-mathbf {y} -\mathbf {X} {\boldsymbol {\beta }})\right).} \rho(\mathbf{y}|\mathbf{X},\boldsymbol\beta,\sigma^{2}) \propto (\sigma^{2})^{-n/2} \exp\left(-\frac{1}{2{\sigma}^{2}}(\mathbf{y}- \mathbf{X} \boldsymbol\beta)^{\rm T}(\mathbf{y}- \mathbf{X} \boldsymbol\beta)\right).
-\]
-!et
-The ordinary least squares solution is to estimate the coefficient vector using the Moore-Penrose pseudoinverse:
-!bt
-\[
-{\hat {\boldsymbol {\beta }}=(\mathbf {X} ^{\rm {T}}\mathbf {X} )^{-1}\mathbf {X} ^{\rm {T}}\mathbf {y} }  \hat{\boldsymbol\beta} = (\mathbf{X}^{\rm T}\mathbf{X})^{-1}\mathbf{X}^{\rm T}\mathbf{y}
-\]
-!et
-where 
-!bt
-\[
-{\displaystyle \mathbf {X} } \mathbf {X}  
-\]
-!et
-is the ${n\times k n \times k$ design matrix, each row of which is a predictor vector $\mathbf {x} _{i}^{\rm {T}} \mathbf{x}_{i}^{\rm T}$ and $\mathbf {y}  \mathbf {y}$  is the column $n$-vector $[y_{1}\;\cdots \;y_{n}]^{\rm {T}} [y_1 \; \cdots \; y_n]^{\rm T}$.
-
+o Bayesian regression analysis
 !eblock
 
