review

Author: dweindl

doc/example/distributions.ipynb

@@ -23,12 +23,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "initial_id",
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -115,7 +113,9 @@
     "\n",
     "    if ax is None:\n",
     "        plt.show()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -125,17 +125,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "4f09e50a3db06d9f",
    "metadata": {},
-   "outputs": [],
    "source": [
     "plot_single(Prior(UNIFORM, (0, 1)))\n",
     "plot_single(Prior(NORMAL, (0, 1)))\n",
     "plot_single(Prior(LAPLACE, (0, 1)))\n",
     "plot_single(Prior(LOG_NORMAL, (0, 1)))\n",
     "plot_single(Prior(LOG_LAPLACE, (1, 0.5)))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -145,18 +145,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "f6192c226f179ef9",
    "metadata": {},
-   "outputs": [],
    "source": [
     "plot(Prior(NORMAL, (10, 2), transformation=LIN))\n",
     "plot(Prior(NORMAL, (10, 2), transformation=LOG))\n",
     "\n",
     "# Note that the log-normal distribution is different\n",
     "#  from a log-transformed normal distribution:\n",
     "plot(Prior(LOG_NORMAL, (10, 2), transformation=LIN))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -166,14 +166,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "34c95268e8921070",
    "metadata": {},
-   "outputs": [],
    "source": [
     "plot(Prior(LOG_NORMAL, (10, 2), transformation=LOG))\n",
     "plot(Prior(PARAMETER_SCALE_NORMAL, (10, 2)))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -183,10 +183,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "5ca940bc24312fc6",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# different, because transformation!=LIN\n",
     "plot(Prior(UNIFORM, (0.01, 2), transformation=LOG10))\n",
@@ -195,7 +193,9 @@
     "# same, because transformation=LIN\n",
     "plot(Prior(UNIFORM, (0.01, 2), transformation=LIN))\n",
     "plot(Prior(PARAMETER_SCALE_UNIFORM, (0.01, 2), transformation=LIN))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -205,10 +205,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "4ac42b1eed759bdd",
    "metadata": {},
-   "outputs": [],
    "source": [
     "plot(Prior(NORMAL, (0, 1), bounds=(-2, 2)))\n",
     "plot(Prior(UNIFORM, (0, 1), bounds=(0.1, 0.9)))\n",
@@ -222,7 +220,15 @@
     "        transformation=LOG10,\n",
     "    )\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "This results in a constant shift in the probability density, compared to the non-truncated version (https://en.wikipedia.org/wiki/Truncated_distribution), such that the probability density still sums to 1.",
+   "id": "67de0cace55617a2"
   },
   {
    "cell_type": "markdown",
@@ -232,10 +238,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "581e1ac431860419",
    "metadata": {},
-   "outputs": [],
    "source": [
     "plot(Prior(NORMAL, (10, 1), bounds=(6, 11), transformation=\"log10\"))\n",
     "plot(\n",
@@ -249,7 +253,9 @@
     "plot(Prior(LAPLACE, (10, 2), bounds=(6, 14)))\n",
     "plot(Prior(LOG_LAPLACE, (1, 0.5), bounds=(0.5, 8)))\n",
     "plot(Prior(LOG_NORMAL, (2, 1), bounds=(0.5, 8)))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {