diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst
index 34a193c..e879931 100644
--- a/docs/source/introduction.rst
+++ b/docs/source/introduction.rst
@@ -15,6 +15,10 @@ Overview
 
 All experimental particle physics results start with the formulation of a physics theory.  Examples of such theories are the Standard Model with a Higgs boson, or supersymmetric extensions of the SM. Once a theory of interest is chosen, an experimental measurement procedure is designed that can be used to to test the theory, or to measure one or more parameters of it. In practice this means that we exploit a software chain of physics simulation software, showering Monte Carlo generators, detector simulation software, detector reconstruction and dedicated analysis tools to reduce a preductions of physics theory to a statistical model for one or more effective observables :math:`\vec{x}`.
 
+.. image:: physics-graph.png
+    :align: center
+    :alt: Illustration of the chain of physics and detector simulation that provide the inputs to the statistical analysis of the data.
+
 A statistical model, or probability model, is mathematical function that assigns a probability :math:`p` to every possible observable outcome :math:`\vec{x}` of an experiment, under the assumption that a particular hypothesis is true. Once you have such a statistical model of your experiment, *all physics knowledge has been abstracted into the model* and all further inference on the theory, or its parameters, is purely procedural and uniquely defined given an unambiguous formulation of the type of statement desired [#]_.
 
 These remaining steps, evaluating the statistical model for the observed data and summarizing the outcome of the evaluation in a convenient form for further interpretation, then result in familiar statements like "The cross-section of squark production is less than 0.3 pb:math:`^{-1}` at 95% confidence Level", "The probability to observed this signal, or more extreme, under hypothesis of no Higgs boson is less than :math:`1.5 \cdot 10^{-8}`, or "The top quark mass has been measured to be :math:`172 \pm 0.9` GeV".
diff --git a/docs/source/physics-graph.png b/docs/source/physics-graph.png
new file mode 100644
index 0000000..937481c
Binary files /dev/null and b/docs/source/physics-graph.png differ
diff --git a/hands_on_2/ex12_build_binned.ipynb b/hands_on_2/ex12_build_binned.ipynb
index e8468a1..1346b73 100644
--- a/hands_on_2/ex12_build_binned.ipynb
+++ b/hands_on_2/ex12_build_binned.ipynb
@@ -7,33 +7,29 @@
     "# Build workspace using histogram templates\n",
     "\n",
     "Build a binned likelihood model version of the ex11 example\n",
-    " * construct a histogram template SH(mgg) with a prediction for the binned signal shape\n",
-    " * construct a histogram template BH(mgg) with a prediction for the binned background shape\n",
-    " * construct a probability model model(mgg) = SH(mgg) + BH(mgg)\n",
+    "\n",
+    "* construct a histogram template SH(mgg) with a prediction for the binned signal shape\n",
+    "* construct a histogram template BH(mgg) with a prediction for the binned background shape\n",
+    "* construct a probability model model(mgg) = SH(mgg) + BH(mgg)\n",
     "\n",
     "This model can be 'seen' in two ways\n",
-    " 1. an extended probability model like ex11 that happen to have binned shapes, i.e.\n",
-    "    * model($m_{\\gamma\\gamma}) = N_\\mathrm{sig}/N_\\mathrm{sig}+N_\\mathrm{bkg} * pdf_{\\mathrm{SH}(m_{\\gamma\\gamma})} +  N_\\mathrm{bkg}/N_\\mathrm{sig}+N_\\mathrm{bkg} * pdf_{\\mathrm{BH}(m_{\\gamma\\gamma})} $\n",
-    "    * $P(N) = N_\\mathrm{sig} + N_\\mathrm{bkg}$\n",
-    "    * where pdf_SH,pdf_BH are probability density functions that follow shape of the unit normalized histograms\n",
-    " 2. A product of Poisson counting experiments for each bin\n",
-    "    * model(vec_N) = Product(i=0..n-1) Poisson(N_i | S_i + B_i)\n",
-    "    * where N_i i=0...N-1 == vec_N are the observed event counts in each bin and S_i and B_i are the predicted signal and background rate in each bin\n",
+    "\n",
+    "1. an extended probability model like ex11 that happen to have binned shapes, i.e.\n",
+    "\n",
+    "  * model(m<sub>γγ</sub>) = N<sub>sig</sub>/N<sub>sig</sub>+N<sub>bkg</sub> * pdf<sub>SH</sub>(m<sub>γγ</sub>) +  N<sub>bkg</sub>/N<sub>sig</sub>+N<sub>bkg</sub> * pdf<sub>BH</sub>(m<sub>γγ</sub>)\n",
+    "  * $P(N) = N_\\mathrm{sig} + N_\\mathrm{bkg}$\n",
+    "  * where pdf_SH,pdf_BH are probability density functions that follow shape of the unit normalized histograms\n",
+    "\n",
+    "2. A product of Poisson counting experiments for each bin\n",
+    "\n",
+    "  * model(vec_N) = Product(i=0..n-1) Poisson(N_i | S_i + B_i)\n",
+    "\n",
+    "  * where N_i i=0...N-1 == vec_N are the observed event counts in each bin and S_i and B_i are the predicted signal and \n",
+    "background rate in each bin\n",
     "\n",
     "Both representations are mathematically equivalent, but expression (2) is in practice faster to calculate because it does not require a normalization calculation over pdf_SH and pdf_BH to happen. While for this very simple example it does not make a noticable difference because the normalization does not depend on any model parameters in scenarios where it does it will effectivelt double the calculation time"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%jsroot"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -43,7 +39,7 @@
   },
   {
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-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
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@@ -71,7 +67,7 @@
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   {
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-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
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@@ -93,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
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@@ -112,7 +108,7 @@
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   {
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-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
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@@ -130,7 +126,7 @@
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    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
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@@ -148,7 +144,7 @@
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   {
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-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
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@@ -182,7 +178,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
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@@ -220,7 +216,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
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@@ -247,7 +243,7 @@
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   {
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-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
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@@ -338,28 +334,9 @@
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jmZMAANRD+YGC53mj0Wg4HBYZQUiNQciHWPoVnLXUuk3y+t1oNHLWhHx4eCge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\nAADAikABAABYESgAAACr/x+qu8TWCRkODgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<IPython.core.display.Image object>"
       ]
      },
      "metadata": {},
@@ -388,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -406,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
@@ -428,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -442,7 +419,7 @@
       "Nuisance Parameters:     RooArgSet:: = (Bscale)\n",
       "PDF:                     RooRealSumPdf::model[ S * sig + B * bkg ] = 2.77715\n",
       "Snapshot:                \n",
-      "  1) 0x7f9248a02520 RooRealVar:: mu = 1 +/- 0.486745  L(-1 - 6)  \"mu\"\n",
+      "  1) 0x7fe4dca0cd80 RooRealVar:: mu = 1 +/- 0.486745  L(-1 - 6)  \"mu\"\n",
       "\n"
      ]
     }
@@ -463,15 +440,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [],
    "source": [
     "w.import(mc);\n",
     "\n",
-    "w.writeToFile(\"../workspaces/model.root\") ; "
+    "w.writeToFile(\"model.root\") ; "
    ]
   }
  ],