diff --git a/analysis_and_scripts/Bachelors_thesis_analyses.ipynb b/analysis_and_scripts/Bachelors_thesis_analyses.ipynb
index a89d4f2f85f0a70ba08149e1a52f4131e133a7d7..c49d8bcbee918954078b288e8f3a87bcfa879c31 100644
--- a/analysis_and_scripts/Bachelors_thesis_analyses.ipynb
+++ b/analysis_and_scripts/Bachelors_thesis_analyses.ipynb
@@ -8,12 +8,12 @@
     "\n",
     "*This Jupyter notebook is for the analyses and model building for Riku Laine's bachelors thesis*\n",
     "\n",
-    "**Contents**\n",
+    "**Contents (links TBA)**\n",
     "\n",
-    "1. [Compas]()\n",
-    "* [Creation of synthetic data]()\n",
+    "1. [Compas data]()\n",
+    "* [Synthetic data]()\n",
     "* [Implementation of competing algorithm]()\n",
-    "* [Implementation of our model]()\n",
+    "* [Implementation of our algorithm]()\n",
     "* [Comparisons and other validation analysis??]()\n",
     "\n",
     "*etc etc...*\n",
@@ -25,18 +25,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(7214, 53)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "(7214, 53)"
+       "<Figure size 1008x504 with 0 Axes>"
       ]
      },
-     "execution_count": 1,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -45,13 +51,14 @@
     "from datetime import datetime\n",
     "import matplotlib.pyplot as plt\n",
     "import scipy.stats as scs\n",
+    "import seaborn as sns\n",
     "%matplotlib inline\n",
     "\n",
     "# Read file\n",
     "compas_raw = pd.read_csv(\"../data/compas-scores-two-years.csv\")\n",
     "\n",
     "# Check dimensions, number of rows should be 7214\n",
-    "compas_raw.shape"
+    "print(compas_raw.shape)"
    ]
   },
   {
@@ -73,7 +80,8 @@
    "source": [
     "# Select columns\n",
     "compas = compas_raw[['age', 'c_charge_degree', 'race', 'age_cat', 'score_text', 'sex', 'priors_count'\n",
-    "                     , 'days_b_screening_arrest', 'decile_score', 'is_recid', 'two_year_recid', 'c_jail_in', 'c_jail_out']]\n",
+    "                     , 'days_b_screening_arrest', 'decile_score', 'is_recid', 'two_year_recid',\n",
+    "                     'c_jail_in', 'c_jail_out']]\n",
     "\n",
     "# Subset values\n",
     "compas = compas.query('days_b_screening_arrest <= 30 and \\\n",
@@ -310,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -334,8 +342,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
@@ -361,6 +371,8 @@
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -369,84 +381,112 @@
        "      <td>69</td>\n",
        "      <td>34</td>\n",
        "      <td>24</td>\n",
+       "      <td>44</td>\n",
+       "      <td>41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c_charge_degree</th>\n",
        "      <td>F</td>\n",
        "      <td>F</td>\n",
        "      <td>F</td>\n",
+       "      <td>M</td>\n",
+       "      <td>F</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>race</th>\n",
        "      <td>Other</td>\n",
        "      <td>African-American</td>\n",
        "      <td>African-American</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Caucasian</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>age_cat</th>\n",
        "      <td>Greater than 45</td>\n",
        "      <td>25 - 45</td>\n",
        "      <td>Less than 25</td>\n",
+       "      <td>25 - 45</td>\n",
+       "      <td>25 - 45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>score_text</th>\n",
        "      <td>Low</td>\n",
        "      <td>Low</td>\n",
        "      <td>Low</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Medium</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sex</th>\n",
        "      <td>Male</td>\n",
        "      <td>Male</td>\n",
        "      <td>Male</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>priors_count</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>days_b_screening_arrest</th>\n",
        "      <td>-1</td>\n",
        "      <td>-1</td>\n",
        "      <td>-1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>decile_score</th>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>is_recid</th>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>two_year_recid</th>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c_jail_in</th>\n",
        "      <td>2013-08-13 06:03:42</td>\n",
        "      <td>2013-01-26 03:45:27</td>\n",
        "      <td>2013-04-13 04:58:34</td>\n",
+       "      <td>2013-11-30 04:50:18</td>\n",
+       "      <td>2014-02-18 05:08:24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c_jail_out</th>\n",
        "      <td>2013-08-14 05:41:20</td>\n",
        "      <td>2013-02-05 05:36:53</td>\n",
        "      <td>2013-04-14 07:02:04</td>\n",
+       "      <td>2013-12-01 12:28:56</td>\n",
+       "      <td>2014-02-24 12:18:30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>length_of_stay</th>\n",
        "      <td>0</td>\n",
        "      <td>10</td>\n",
        "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -469,39 +509,47 @@
        "c_jail_out               2013-08-14 05:41:20  2013-02-05 05:36:53   \n",
        "length_of_stay                             0                   10   \n",
        "\n",
-       "                                           2  \n",
-       "age                                       24  \n",
+       "                                           2                    5  \\\n",
+       "age                                       24                   44   \n",
+       "c_charge_degree                            F                    M   \n",
+       "race                        African-American                Other   \n",
+       "age_cat                         Less than 25              25 - 45   \n",
+       "score_text                               Low                  Low   \n",
+       "sex                                     Male                 Male   \n",
+       "priors_count                               4                    0   \n",
+       "days_b_screening_arrest                   -1                    0   \n",
+       "decile_score                               4                    1   \n",
+       "is_recid                                   1                    0   \n",
+       "two_year_recid                             1                    0   \n",
+       "c_jail_in                2013-04-13 04:58:34  2013-11-30 04:50:18   \n",
+       "c_jail_out               2013-04-14 07:02:04  2013-12-01 12:28:56   \n",
+       "length_of_stay                             1                    1   \n",
+       "\n",
+       "                                           6  \n",
+       "age                                       41  \n",
        "c_charge_degree                            F  \n",
-       "race                        African-American  \n",
-       "age_cat                         Less than 25  \n",
-       "score_text                               Low  \n",
+       "race                               Caucasian  \n",
+       "age_cat                              25 - 45  \n",
+       "score_text                            Medium  \n",
        "sex                                     Male  \n",
-       "priors_count                               4  \n",
+       "priors_count                              14  \n",
        "days_b_screening_arrest                   -1  \n",
-       "decile_score                               4  \n",
+       "decile_score                               6  \n",
        "is_recid                                   1  \n",
        "two_year_recid                             1  \n",
-       "c_jail_in                2013-04-13 04:58:34  \n",
-       "c_jail_out               2013-04-14 07:02:04  \n",
-       "length_of_stay                             1  "
+       "c_jail_in                2014-02-18 05:08:24  \n",
+       "c_jail_out               2014-02-24 12:18:30  \n",
+       "length_of_stay                             6  "
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
-      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 1008x504 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -511,12 +559,225 @@
     }
    ],
    "source": [
-    "import seaborn as sns\n",
-    "display(compas.head(3).T)\n",
-    "sns.kdeplot(np.array(compas_raw.age))\n",
+    "display(compas.head(5).T)\n",
+    "plt.figure(figsize = (14,7))\n",
+    "sns.kdeplot(compas_raw.age.values)\n",
+    "plt.title(\"Density plot of defendants' ages\")\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>is_recid</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age_cat</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>25 - 45</th>\n",
+       "      <td>1784</td>\n",
+       "      <td>1748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Greater than 45</th>\n",
+       "      <td>847</td>\n",
+       "      <td>446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Less than 25</th>\n",
+       "      <td>551</td>\n",
+       "      <td>796</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "is_recid            0     1\n",
+       "age_cat                    \n",
+       "25 - 45          1784  1748\n",
+       "Greater than 45   847   446\n",
+       "Less than 25      551   796"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>is_recid</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>740</td>\n",
+       "      <td>435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>2442</td>\n",
+       "      <td>2555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "is_recid     0     1\n",
+       "sex                 \n",
+       "Female     740   435\n",
+       "Male      2442  2555"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>is_recid</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>race</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>African-American</th>\n",
+       "      <td>1402</td>\n",
+       "      <td>1773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Asian</th>\n",
+       "      <td>21</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Caucasian</th>\n",
+       "      <td>1229</td>\n",
+       "      <td>874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hispanic</th>\n",
+       "      <td>312</td>\n",
+       "      <td>197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Native American</th>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Other</th>\n",
+       "      <td>213</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "is_recid             0     1\n",
+       "race                        \n",
+       "African-American  1402  1773\n",
+       "Asian               21    10\n",
+       "Caucasian         1229   874\n",
+       "Hispanic           312   197\n",
+       "Native American      5     6\n",
+       "Other              213   130"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tab = compas.groupby(['age_cat', 'is_recid']).size()\n",
+    "display(tab.unstack())\n",
+    "\n",
+    "tab = compas.groupby(['sex', 'is_recid']).size()\n",
+    "display(tab.unstack())\n",
+    "\n",
+    "tab = compas.groupby(['race', 'is_recid']).size()\n",
+    "display(tab.unstack())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -545,28 +806,36 @@
    "source": [
     "import numpy.random as npr\n",
     "\n",
+    "# Set seed for reproducibility\n",
     "npr.seed(0)\n",
     "\n",
+    "# Set number of judges and the number of defendants assigned to them\n",
     "nJudges_M = 100\n",
     "nSubjects_N = 500\n",
     "\n",
+    "# Set coefficient weights\n",
     "beta_X = 1.0\n",
     "beta_Z = 1.0\n",
     "beta_W = 0.2\n",
     "\n",
+    "# Assign judge IDs as running numbering from 0 to nJudges_M - 1\n",
     "judgeID_J = np.repeat(np.arange(0, nJudges_M, dtype = np.int32), nSubjects_N)\n",
     "\n",
+    "# Sample acceptance rates uniformly from a closed interval\n",
+    "# from 0.1 to 0.9 and round to tenth decimal place.\n",
     "acceptance_rates = np.round(npr.uniform(.1, .9, nJudges_M), 10)\n",
     "\n",
+    "# Replicate the rates so they can be attached to  the corresponding judge ID.\n",
     "acceptanceRate_R = np.repeat(acceptance_rates, nSubjects_N)\n",
     "\n",
+    "# Sample the variables from standard Gaussian distributions.\n",
     "X = npr.normal(size = nJudges_M * nSubjects_N)\n",
     "Z = npr.normal(size = nJudges_M * nSubjects_N)\n",
     "W = npr.normal(size = nJudges_M * nSubjects_N)\n",
     "\n",
     "probabilities_Y = 1 / (1 + np.exp(-(beta_X * X + beta_Z * Z + beta_W * W)))\n",
     "\n",
-    "# 0 if P(Y = 0| X = x;Z = z;W = w) >= 0.5 , 1 otherwise\n",
+    "# 0 if P(Y = 0| X = x; Z = z; W = w) >= 0.5 , 1 otherwise\n",
     "result_Y = 1 - probabilities_Y.round()\n",
     "\n",
     "probabilities_T = 1 / (1 + np.exp(-(beta_X * X + beta_Z * Z)))\n",
@@ -605,7 +874,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -887,7 +1156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {
     "scrolled": false
    },
@@ -974,7 +1243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {
     "scrolled": false
    },