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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9b22c91b-aa50-4807-a2c4-5d4ec91b7747",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "(hypothesis-testing)=\n",
+    "# Bayesian Hypothesis Testing - an introduction\n",
+    "\n",
+    ":::{post} December 2024\n",
+    ":tags: hypothesis testing, bayesian decision theory\n",
+    ":category: beginner \n",
+    ":author: Benjamin T. Vincent\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a71c0158",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pymc as pm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e8c93da2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "plt.rcParams.update({\"font.size\": 14, \"figure.constrained_layout.use\": False})\n",
+    "SEED = 42\n",
+    "rng = np.random.default_rng(SEED)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a551c9b3",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "Bayesian hypothesis testing provides a flexible and intuitive way to assess whether parameters differ from specified values. Unlike classical methods focusing on p-values, Bayesian methods let us directly compute probabilities of hypotheses and quantify the strength of evidence in various ways.\n",
+    "\n",
+    "In this tutorial, we'll use PyMC to:\n",
+    "\n",
+    "* Fit a simple model to synthetic data and obtain samples from the prior and posterior distributions.\n",
+    "* Demonstrate the following Bayesian hypothesis testing methods:\n",
+    "  * Posterior probability statements\n",
+    "  * Highest Density Intervals (HDIs)\n",
+    "  * Regions of Practical Equivalence (ROPE)\n",
+    "  * Bayes Factors\n",
+    "\n",
+    "We'll work through a scenario where we want to know if the mean of some metric (e.g., monthly profit) is different from zero."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf2a9ed5",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "Many Bayesian practitioners argue that collapsing a rich posterior distribution into a single binary decision (e.g., \"reject\" or \"fail to reject\") undermines the essence of Bayesian inference. The Bayesian perspective values the entire posterior as a nuanced representation of uncertainty about parameters. Reducing it to yes/no decisions discards that nuance and may mislead by oversimplifying the uncertainty involved.\n",
+    "\n",
+    "However, in real-world scenarios—such as policy-making, resource allocation, or medical decision-making—practitioners often must choose a single course of action. In such cases, translating the posterior into a decision rule or threshold is necessary. The key is to do so transparently and thoughtfully, ideally incorporating utilities, costs, and the full breadth of uncertainty in the decision process.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6292d775",
+   "metadata": {},
+   "source": [
+    "### Parameter estimation vs model comparison\n",
+    "\n",
+    "The Bayesian evaluation of null values can proceed in two distinct ways (see {cite:t}`kruschke2011bayesian`):\n",
+    "\n",
+    "#### Parameter estimation\n",
+    "The parameter estimation approach considers a model where the parameter is allowed to vary (which includes the null value). We then compute the posterior distribution of this value and come up with some kind of decision rule which determines if we accept or reject the null value.\n",
+    "\n",
+    "#### Model comparison\n",
+    "Two competing models are considered. The first model assumes that the null value is true, or fixed, and the second model allows a range of values. The models are compared to see which is more supported by the data. An example would be in assessing if a coin is fair (null hypothesis) or biased (alternative hypothesis) - we would set up a model where the coin has a fixed probability of heads (0.5) and another model where the probability of heads is a free parameter. Readers are referred to the PyMC examples focussing on {ref}`pymc:model_comparison` for more details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c268654",
+   "metadata": {},
+   "source": [
+    "## Setting up the example\n",
+    "\n",
+    "Rather than focus on a complex example, we'll pick a trivial one were we are simply estimating the mean of a single variable. This will allow us to focus on the hypothesis testing. The important thing is what we do with our MCMC samples, not the particulars of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3f9fabda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1400x100 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 171,
+       "width": 1105
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "true_mu = 2.0\n",
+    "true_sigma = 3.0\n",
+    "n = 12\n",
+    "\n",
+    "x = rng.normal(loc=true_mu, scale=true_sigma, size=n)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(14, 1))\n",
+    "ax.plot(x, np.zeros_like(x), \"ro\")\n",
+    "ax.set(yticklabels=[], yticks=[], xlabel=\"x\", title=\"Observations\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "159213a0",
+   "metadata": {},
+   "source": [
+    "## Sampling from the prior and posterior\n",
+    "\n",
+    "Now we'll build our simple model. Again, the focus here is not on the model of the data as such, but simply obtaining a meaningful prior and posterior distribution. We'll ask for more MCMC samples than we normally do, so that we can get a more accurate approximation of the prior and posterior distributions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bfaed521",
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [mu, sigma, y]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [mu, sigma]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4b31a55016f480280c467660c117f57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 2 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with pm.Model() as model:\n",
+    "    # priors\n",
+    "    mu = pm.Normal(\"mu\", mu=0, sigma=2)\n",
+    "    sigma = pm.Gamma(\"sigma\", alpha=2, beta=1)\n",
+    "    # likelihood\n",
+    "    pm.Normal(\"y\", mu=mu, sigma=sigma, observed=x)\n",
+    "    # sample\n",
+    "    idata = pm.sample_prior_predictive(samples=10_000)\n",
+    "    idata.extend(pm.sample(draws=10_000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "790c4959",
+   "metadata": {},
+   "source": [
+    "We didn't get any warnings from the sampling processes.\n",
+    "\n",
+    "It is good practice to visualise the posterior distribution, so below we'll look at the joint posterior over $\\mu$ and $\\sigma$ parameters. Everything looks fine here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "59a1800f",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 720x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 439,
+       "width": 620
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_pair(idata, var_names=[\"mu\", \"sigma\"], marginals=True, point_estimate=\"mean\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e55dda73",
+   "metadata": {},
+   "source": [
+    "Finally, seeing as $\\mu$ is the core parameter of interest, we'll visualise both the prior and posterior distributions for $\\mu$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c4d2dc2d",
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dnDdvnuX5VOTvNkdU9DmG4/2pzOtq93UgEj/zo3VOVUY0X29J2r59uwYNGhQcN23aVDNnzqzCMwjNCSecYGlEmjNnTkhN+Ec8+eSTevfdd4Pjhx56SNdcc01YMwIAAACxjOWuAAAAAISdz+fTww8/bGnQkaQzzzwz5Me48cYbI35zd9myZZabKZJ0xx13lHszRZIyMjL0+uuvW2bQyMnJ0TvvvFPhDNF4nke76KKLQm7QkQ7PRHHnnXdq6NChwVpOTo6++OKLSh3/73//+zGbITIyMnTHHXdYasdajmbRokUlfjv+iSeeKPcGvSQNGzZMd955Z7nbRMPpp5+uUaNGlXkjWjq8HMRrr71m2Wb//v36+OOPy9wnmp/x1atXW5odhgwZEvJNU0lq2LBhRG6aHi07O1svvfSSpfavf/3rmJ9JSerSpYteeukly2vyzjvvqKioqMI5In3e17RrWyyf//FwXpQn2j+jjhap62JNEQvXgUj8zI/VcyoWXu9o2Lp1q6VBJz09vUINOtLh9/BonTp1Cks2AAAAIF7QpAMAAAAgrLZv364bb7xR8+bNs9TT0tJ0+eWXh/QY9evX11/+8pdIxLMo/pvzrVq10i233BLSvl27di1x4+WTTz5Rfn5+yMeP1vMMh5tuuskyLv7+hqJdu3Yh/6b0JZdcYrlh9fvvv2vfvn1lbv/RRx9Zxn379tUFF1wQ0rFuvvlmNW/ePKRtI8HlcmnkyJEyDOOY23bp0kV//vOfLbUPP/xQgUCg1O2j+RkvvrzHsWZ4sMMHH3xgma1h0KBBOu+880Lev0ePHpbP1Z49e/TDDz9UKEM0zvuadm2L5fM/Hs6Lstj5MyqS18Wawu7rQKR+5sfqOWX36x0tK1assIwrulSVaZo06QAAAKDGo0kHAAAAQMj27NmjDz74oMSfCRMm6L///a9uuOEGnX322frll18s+zkcDv3zn/9UUlJSSMf505/+pISEhEg8BYvZs2dbxpdddpk8Hk/I+48YMcJyAzE7O1tLliwJef9oPc9waNu2rWV5q6VLl1b4MYYPHx7SDVdJqlevntq0aWOpbdy4scztizdKXHHFFSHn8ng8Gj58eMjbh9spp5xSoSaB4s9tx44dWr9+fanbRvMzXqdOHcu4+I28WDBlyhTLeMSIERV+jHPPPdcyPnoJm1BE47yvade2WD7/4+G8KIudP6MieV2sKey+DkTqZ36snlN2v97RsnLlSsu4ok06W7ZsUU5OTnDcvHlz1a5dOyzZAAAAgHjhOvYmAAAAAHDY5s2bNXr06Art4/F49Mgjj1Roqat+/fpVMFnFbdy4UVlZWZba4MGDK/QYzZs3V+fOnS03LBYtWqQTTzwxpP2j8TxDUVRUpI0bN2rXrl3Kzc1VXl5eiaXKJFl+y3337t0KBAJyOEL/3Y++fftWKFfz5s21bt264Lj4b88fsWnTJst76XQ6dfrpp1foWIMGDdJzzz1XoX3CZdCgQRXavl27dmrZsqW2bNkSrC1btkwdO3a0bBftz3i3bt1kGEZwGZKffvpJL7zwgm6//fYK3aiMlMzMTMtN+4SEhAp/JqXDs3YcraI3USN93te0a1usn/+xfl6Ux86fUZG6LtYUsXAdiNTP/Fg8p2Lh9Y6WqjbprFq1qkr7AwAAANUBTToAAAAAIqZv3756+OGHlZGRUaH9OnToEKFEf1i7dq1lXK9evUotedK1a1fLDYvij1ueaDzPsmRlZenzzz/X1KlTtXLlSvl8vgrtb5qmDh06pLp164a8T9OmTSt0jOTkZMv46N+8PtqaNWss4zZt2igxMbFCx2rdurWSkpKUl5dXof3CoTI3qDp37my5GV38NZCi/xlPS0vTkCFD9O233wZrr7zyij7++GOde+65GjBggHr37l3ifY2WZcuWBW/qSlLt2rX18ccfV/hxii+hs3fv3grtH+nzvqZd22L9/I/186I8dv6MitR1saaIhetApH7mx+I5FQuvd7RUtcmm+P4sdQUAAICaiCYdAAAAAFXmcrmUkpKiOnXqqG3bturRo4fOPPNMtW/fvlKPV5HGj8o6ePCgZVzRm0lHNGvWrNzHLU80nmdpPvnkE/373/8u8VvfFZWbm1uh51DR5QycTqdlXLw54ojiz6NJkyYVOo50eEm24447Ths2bKjwvlVVmbzF9yntvbTjMz5q1CitWLFCO3bsCNb279+vCRMmaMKECXK5XOrQoYN69+6tvn376sQTTyyxdEmk7Nu3r8S4ojODlaYi57wU+fO+pl3b4uH8j+Xzojx2/YySInddrCli4ToQqZ/5UuydU7HwekfDtm3bLJnq1q1bIvOxrF692jKmSQcAAAA1EU06AAAAAELWt29fTZgwIeLHicZvPxdfRqGyxyx+E6oiN1TsmDnh5Zdf1osvvhiWxyrvBlppDMMIy3GLO3TokGWckpJSqcep6A3FcKlM3lA+d3Z8xtPT0zVp0iQ9+uijmjZtWonv+3w+rVq1SqtWrdLEiRPldrs1YMAAXXfddTr++OMrlS9UkbrZmZ+fX6HtI33e17RrWzyc/7F8XpTHztl9InVdrCli4ToQqZ/5UuydU7HwekdD8aWuKtNgw0w6AAAAAE06AAAAACApfDeTInlTqqp+/fXXEg06Ho9HgwcPVr9+/dSxY0c1atRItWvXVkJCgtxut2XbgQMHWn5rPVZV9j04eimkWFeZrNH6jNevX19jx47VmjVrNHnyZM2aNUubN28udVuv16vp06dr+vTpOv/88/XYY49VusniWLxeb0QeN9bVhGvb0WL1/I/V86I6iadreLRVx+tALJ9T1fH1lko26VR0qatdu3bpwIEDwXH9+vXVqFGjsGQDAAAA4glNOgAAAABqpOLLHuTk5FTqcYrP4hALS5SU5YUXXrCMO3bsqFdeeSXkZRlyc3MjkKrqiv/mefH3JFSV/QxUVU5OjtLS0iq8z9FKW5bG7s94RkaGHnroIT300EPas2ePFixYoEWLFmnhwoVavXp1iRvqU6ZM0d69e/X222+XWPYkHIq/RieeeKLeeeedsB/Hbna/79EWb+d/rJ0XsSpS18VwqehMctFWk64DsXBO1ZTXu3iTTpcuXSq0P7PoAAAAAIfRpAMAAACgRqpXr55lXNkZYrZv324ZR/KmYFXs379fixYtCo6dTqfGjh0bcoOOz+er9M3vSCv+Xu7cubPCjxEIBLRr164wJaqYnTt3VvhmdPHnWPw1KK1m52e8YcOGOvfcc3XuuedKkvbt26evv/5ab7/9tiXXvHnzNHnyZA0bNqxSWctT/DXetm1b2I8RC2LpfY+GeD7/Y+G8iFWRui5KksPhsIz9fn+FjiOVXN4o1tS068ARdp1TNeX1rupyV8uXL7eMKzoTDwAAAFBdOI69CQAAAABUPx06dLCMs7KyKnXTfsWKFZZxx44dq5QrUtatW2f5zf8ePXqoRYsWIe+/atWqSt3IjIbir/nGjRuVn59focfYtGmT8vLywhkrZMV/s7wy+5T2uYvlz3iDBg00YsQITZkyRd27d7d878svv6zy45em+M3E7du329aYFUmx/L5HQryf/0ez47yIVZG6LkpScnKyZVzR9/7gwYMxO7PcETXtOlCWaJ1TNeH13rFjh7KysoLjhIQEtW7dukKP8euvv1rGzKQDAACAmoomHQAAAAA1Ups2bZSammqpTZ8+vUKPsX379hI3BXv37l3lbJFw4MABy7hJkyYV2v/7778PZ5ywat26teW3zf1+v3744YcKPcaMGTPCnCpyx/7tt9+0ZcsWS634zUcpPj7jSUlJ+stf/mKprVu3rsztiy9NUpElZ5o3b67mzZtbat98803I+8eLeHjfwynez//SVPS8kKp2bsSiSF0XpZJLpFV01pPijQahiPb7U9OuA8dSmXOqImrC6118Fp3jjjuuxKxU5dm7d6+WLFliqdGkAwAAgJqKJh0AAAAANdZpp51mGX/44Yfyer0h7z9x4kSZphkc16lTR7169QpbvnByu92WcUWW6sjJydGHH34Y7khhYxiGBgwYYKm9//77Ie9fVFSkSZMmhTtWyObMmVOh37gv/tyaNm1a4rf4j4iHz3izZs0s4/JmtSg+A0ZOTk6FjnXWWWdZxuPGjYv5GTEqIx7e93CJ9/O/LBU5L6SqnxuxJpLXxSZNmigxMTE4zs7O1vr160M+VmV+Htrx/tSk60AoKnpOVVR1f72LN+nUqlWrQvt/9NFH8vl8wXFSUpJatmwZlmwAAABAvKFJBwAAAECNddVVV1nGmzdv1uuvvx7SvqtWrdKECRMstWHDhlX4pkW0NG7c2DJeuHBhyDcJH3/88RIz8cSayy67zDKeP3++vvrqq5D2ff3117V169ZIxAqJz+fTE088Ybk5V5aVK1eWuEE8fPjwMn+bPZqf8aNvvlXExo0bLeP09PQyt61fv75lvGHDhgod6/rrr1dSUlJwvG/fPj3wwAMhvfbxpCZd26TYPv+jcV5IVT83Yk0kr4tOp1OdO3e21D755JOQcn333Xf68ccfQ9r2aHa8P9X1OhCtc6qiquvrfUTxpbh+//33kH927tixQ2+++aallpGRUaGZeAAAAIDqhL8JAwAAAKixunfvrlNOOcVSGzNmjD7++ONy91u3bp1uuukmy42i2rVr65prrolIznDo3LmzUlJSguPc3FyNHj263CU3fD6fRo8erS+++CIaEavk+OOPV9++fS21kSNH6pdffil3v08++URjx46NZLSQ/PDDD3riiSfk9/vL3GbDhg265ZZbLNukpqaWaFA4WjQ/488995zuvfdeLV26tNzHPlpmZqZeeOEFS61fv35lbl/8xvrnn39eodkQ6tevr1tuucVS++6773TbbbcpMzMz5McpKirSF198oaFDh8ZkA1tNurZJsX3+R+O8kKp+bsSiSF0XpZKzar333nvHXMZq1qxZuv/++0NIXpId7091vQ5E65yqqOr6eh9RfCmurKw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+f9fv98r0VH+xLGqN/vn1T+h4ed0NyOlkldc5FtzJVWzZ1FiuHrnH7+emPNChGJdCUz4h96tfShS5C3fLdXqbor0npbBhAAAAAJpTs4V0Bg0alLR++vTpBte9ePGiQqFQYn3gwIFN1q6rXb58WY888khS+yZNmqQf//jH8nq9zfa6AAAAAACg44lErE6fkY4elY4ek44etTpyVDp1OhZMaSZulzNlU+fOUpcusUdnqUsXZ0qnnBwnkBMP5ni9qQ2MGGOUm+uM0lxcHLruUNXRqBPgKSmVSkqkK1eqH8VXrLNcq9wJBDVGtqdEfz/w90lly07fruPlA65Z50qJ8zh2rGZp3T9LRoatd9qteHlmpvNvBKBtCw+9TdG3fiBXSfW1ad/WX6uKkA4AAADQbjVbSKdbt27Kzs5WaWmpJGn//v0Nrrtv376k9eYK6ZSVlenRRx/V4cOHE2UjR47Uz372M6WlpTXLawIAAAAAgPYvGrU6WxgP4lSHcU6ckILNEMZJS5MKCqSC7s5zjwJTvd7dCXe4XO0z1OFyGWVkOKP/FNQajLnun9laq8pKqahIulx09bNV0VVlZWXSBwf+Xtne0sQxotboFwcea7Kfo6LCeZyq9T232qEen1fKzbOJkXhy8+Kj8hjldVbStFudsiW3u33+3wNtnsuj0ISH5V/zrUSR58hqmUuHZbsMuk5FAAAAAG1Vs4V0JGnixIlas2aNJOnSpUs6ceKE+vbtW2+97du3J61Pnjy5ydtWVVWlxx57THv37k2UDR48WL/85S+VlZXV5K8HAAAAAADap0DA6uhR6cAh6cBBq0OHpMOHpcqqpn2drvlSnz5Sn95Sr15GPQpiwZwCKTeHkVUaw5jqYE+vXrW21to/WFaqnN/8t1Rj9J0jabdp8u2DNahIunxZKi52Qj1XrkjRaHO23gl6nT/vPJLVDvS4XFJujq0V3snLNYnlxHRceakfRQnoaEKj7pNvw09kAlcSZb5tv1Fg8b+lsFUAAAAAmkuzhnQWLFiQCOlI0tKlS/WJT3yi3nrLli1LLPv9fs2cObNJ2xUKhfS5z31OW7duTZT17dtXv/71r9W5c+cmfS0AAAAAANB+lJZaHTwkHYwFcg4elI4flyJNFMrIypL69omHcYz69JH69naCJBkZhCdSJXPv/8gdLEkq63n/4/psV1etfSMRq5ISJ7Bz+bIzEk9RsXT5sq2xXF3enNOcSU5g6HJsVKBkdU+7lZ1tnTBPbHq0bl2lggKTGKWpoLuUlcXvItBkfJkKjf07+Tb/LFHk2f+CgjM/J5vZNYUNAwAAANAcmjWkM3/+fH3jG99QKHa14dlnn9Ujjzwir9d7zTobNmzQ0aNHE+tz585VRkZGk7UpGo3qy1/+stauXZsoKygo0DPPPKPu3WuNiQwAAAAAADqo8nKrdw9I+/ZL77zjLJ8tbJpj5+VJAwdIA/pLAwYY9e8n9esr5TAiTusTLJdv2zNJReHBixTtOrTO3d1uo7w85/94UNIM7rX/X621KitTUnjHmWLLJk23FV+urGy6H+taSkudx4mTSS1N2icr06p7gdSzQOrdW+rd26hPbydg1qULv8NAY4XGPyjvtl/LRJzr6CYSknfHHxSc9URqGwYAAACgyTVrSCc/P1/333+//vCHP0iSTpw4oZ///Of69Kc/Xef+gUBA3/zmNxPrxhg9/vjj1zz+qVOntGDBgsR6r169tGrVquu26etf/7peeeWVxHqXLl30zDPPqHfv3g36mQAAAAAAQPsTDFodOiztf0fa/47VO+9Ix09Itu7BRhqsU6d4EEca0N8klvNyCTG0Fd6d/yNTdSWpLDjt2terGsMYo+xsKTtbSp4hvu7fj6qq5PBOdYjH1gr0lJTUeYgmUVYulR12pnVzVP+hpKdJvXtb9e4t9e/nhNAGDpB695I8Hn7vgbrYzK4KD79b3rf/kijz7vqjglM+LvkyU9gyAAAAAE2tWUM6kvTYY4/p+eefV3l5uSTpqaeeUkZGhh5++GG5XNVDAl++fFmf//zndejQoUTZHXfcoREjRjRZW5588kn96U9/Sqzn5OTo17/+tQYOHHidWgAAAAAAoD2x1urkSWnvPmlfLJBz8JAUDt/ccXv3loYOkYYMNhoyWBo8WOrSmVFF2rRQRe1RdAYtULTb8JQ0Jy3NqEcPqUePq7fU/h0Lh62Ki2tMu1UcC/BctjWWnfLioqabsq2ySokp4RxOgMfrlfr2tRo0oDq4M2Sw1LUrfyOAJAUnfiQppGMCV+R9+zmFJjyUwlYBAAAAaGrNHtLp3r27nnzyST3++OOKRqOy1uo73/mO/vjHP2ratGnKzc3V8ePHtXr1alVVVSXqDR48WN/4xjeatC0/+9nPktZLSkp03333Nfo4+/bta6omAQAAAACAZhYIWL3zrrTnbWnP21Zvvy1duYlRRjweZ6qqIUOkoYONhgyRBg+SMjIIGrQ33l1/kqksSiprqlF0mpvHY5SfL+XnX72l9u9pNGpVUuIEeoqLq6fdunjJ6lyhVHjOeVy6dOPtCYWckXec0XeqR97pnCcNG2Y1bKh0yzCjYUOdNhPcQUdjuwxSeOCt8hxZnSjz7vy9QuMflIzrOjUBAAAAtCXNHtKRpHnz5unb3/62vv71r6syNnn2sWPHdOzYsTr3Hz58uH784x8rKyurWdtlrVUkEmnW1wAAAAAAAC3r0iXrBHL2Wu3ZIx04eOOj5Hg8TgDnlluk4bcYDR3iTOHj9RIgaPdClfJu/VVSUXjAXEW7j0xRg5qPy2WUmyvl5l69Jfn3PBCwOn9BKiyUzp2TTp9xRqU6eVo6dUoKBBr/2peLpA0bnUc8vNM5T7plmNXIkUajR0nDb5HS0/mbQ/sXmviRpJCOq/iE3EffUGTgvNQ1CgAAAECTapGQjiTdc889GjNmjL7//e9r7dq1CoVCtfbp2rWr7r//fn3yk5+Uz+drqaYBAAAAAIA2ylqrM2elHTulHTutdu+Rzp698eP16ysNHy4NH2Y0fLg0aKDk9xMO6Ii8e/4sV0Xy0DHBaZ9KUWtaB7/fqE9vqU/veEn130Y0anXxonTyVOxx0uroMenIUenixca9zuUiaf1Gaf1GJ7TjdkmDB1uNHi2NjgV3unXj7xLtT6T3ZEXyh8l98d1EmXfH7wnpAAAAAO2Isdba+ndrWkVFRdq+fbsKCwtVXl6u/Px89enTRxMmTJDb7W7p5rSooqKi+ndqp4wxyo19Jau4uFgp+NUD0IrQJwCIoz8AEEd/gIY6e9Zqx05p+07n+dy5GztOly7SyBHOCDkjhkvDhkpZWdz4bw1S3h+EqpTx60VylVenS8L9Z6vqvp+3bDvaiStXqgM7R45YHTkqHT4ilZff+DG7d5fGj5MmjDMaP07q0YO/3fYq5f1BC/Ps+YvSln81qaz84ZdluwxKUYuA1qOj9QcAro8+AUBcS/QHeXl5TXasFhtJp6a8vDwtWLAgFS8NAAAAAADamHPnrbbvcEbK2bFDOlvY+GO4XM6oOKNGSaNHGY0eKRUUOBdygKt53/5rUkBHkoLTHk9Ra9q+nByjcWOlcWOl+Og70ajVqdPSuwekd9+1zvMBqaKiYcc8d05aukxausy5+NqjwGr8eGn8OKMJ46XujLSDNip8y12yb35fpqo4Uebb+XsFFvxr6hoFAAAAoMmkJKQDAAAAAABwLSWlVtu3S5u3Wm3bJp0+0/hjZGRIo0ZKo2JT44wYLmVmctMeDRAOyrvlF8lFfacr2nN8ihrUPrlcRn37SH37SIsWJAd33nlX2rffas8e6dAhKRKt/3hnC6Wzr0mvvuaEdnr3tpo6WZo6xRlpJz2dv3+0Ed40hcbcL9/m6pG7PHtfUGDmE1JaTuraBQAAAKBJENIBAAAAAAApFQ5b7X9H2rzFavMWaf87UrQBN+Vrystzpr4ZN9ZozGhpQH/J7eamPBrPs/evcpUlz6EWnPapFLWmY6kZ3Fm80Pn7rax0+oc9b0tv77Xa87ZUVlb/sU6dch5//ZuV1yuNGW01dYrR1CnSwAGMooXWLTT2g/Ju+ZWMjUiSTLhS3refU2jSIyluGQAAAICbRUgHAAAAAAC0uNNnrLZsiY2Ws10qL29c/ZxOTihn/Hhnapv+/bjpjiYQCcq3+apRdPpMVbT3pBQ1COnpzt/4hPGSZBSNWh07Lu3YKe3YYbVjp3Sl5PrHCIWkbdulbdut/uunUn6+NH2a1ZxZzrH9fvoOtC42u0DhIYvkPbA0Uebd+QeFJnxYcrlT2DIAAAAAN4uQDgAAAAAAaHaBgBPG2bDJCeecOt24+p06SePGShPGO1PXDOjvjLoBNCXP3uflKj2bVBZiFJ1WxeUyGjjAGQ3nffc6oZ2jx6TtO6QdO53QTmnp9Y9x8aL00svSSy9bpadLU6c4gZ1p06RO2fQraB1CEz6cFNJxlZyW+8hqRQYvTGGrAAAAANwsQjoAAAAAAKBZFJ6z2rBR2rDBaut2KRhseF2fzwnlTJ5kNGmiNGggoRw0s0hIvs0/Ty7qNVGR3pNT1CA0hMtlNGig00d84H1OaOfgIWnzFmnTZmd6rEjk2vUrK6U1a6U1a63cbmn8OKvZM41mzZK6d6PPQepEe4xTpPsouc+9nSjz7vg9IR0AAACgjSOkAwAAAAAAmkQkYrV3n7R+gxPOOXykcfUHDZSmTJamTDYaM5opaNCyPPtflKskeYin4LRPSUyj1qa4XEbDhkrDhkoPPWhUXu6M4rV5i9WmzdLZwmvXjUSkrdukrdusfvAjadhQq9mzjGbPckbuYUo9tChjFBr/IbmXfiVR5Dm5Sa4L7yradVgKGwYAAADgZhDSAQAAAAAAN6yk1LnxvWGD1cbNUklJw+vm5kpTJkmTJxtNniTld+EGOFIkGpZv08+SiiI9xinSd3qKGoSmkplpNGe2NGe2kbVWJ09K6zZIb77ljLJj7bXrvntAeveA1S9/LfXrK82/1WrBfKP+/eir0DLCQ29X9I3vyVVxMVHm3fF7BRb/WwpbBQAAAOBmENIBAAAAAACNcv681ZtvSW+8ZbVzpxSJNqyeyyWNHiVNm2o0dYo0eBBTWKF18LzzslxXTiaVBad/mlF02hljjPr2lfr2lT74gFFRkdW69dKb66y2bJGCoWvXPX5Ceua30jO/tRo00Gr+rUYLbpV69+Z3BM3I41N4zAPybfxJddH+lxSY/Q9Sel4KGwYAAADgRhHSAQAAAAAA9Tp23OqNN53RJ/a/0/B6nTpJ06ZI06cbTZ0sderEDW20MtGIfBt/mlQUKRijSL+ZKWoQWkpentFdd0p33WlUUWG1ZasTPly/QSotvXa9w0ekw0esfvEraehQq4XzjRYtkLp2pX9D0wuNfUDezT+XiTopMhMJyLvnWYWmfCLFLQMAAABwIwjpAAAAAACAWqJRJ4zzxpvOqDknTtZfJ27QQGnGdGn6NKORIyS3mxvXaL0877wiV/HxpLLgtMcZRaeDycgwmjtHmjvHKBy22r3HCey88aZ0/vy16x04IB04YPX0z6RJE61uW2w0Z5ZzPKAp2MyuCg9bIu/+lxJl3p3/q9Ckj0ouLu8DAAAAbQ1n8QAAAAAAQJIUiVjt3CWtXmv11jrp4sWG1fP5pEkTnNFypk+TCrpzcxptRDQs38b/SiqKdB+pyIC5KWoQWgOPx2jCeGnCeKPPfdpq335p5SqrVWukS5fqrmOttGWrtGWrVXqaNGeO1ZLFznEIKuJmhcY/lBTScZUVyn1opSJDb0thqwAAAADcCEI6AAAAAAB0YJGI1Z63pZWrrdaslYqKGlYvK0uaOV2aM9toymQpPZ2b0Gh7PPtfrmMUnU8xig4SXC6jUSOlUSONPvMpZ4SdeH9ZXFx3ncoqadnr0rLXrfLzpcULncDOwIH8XuHGRAtGK9JjnNxndybKfDv+W5WEdAAAAIA2h5AOAAAAAAAdTDRq9fZeadUaq9Vrrj0yxNW65kuzZ0mzZxmNH+eMNgG0WZGQfBufTi7qPkqRgbemqEFo7dxup+8bP87oic9a7dgpLV/pBHYqKuquc/Gi9D9/lP7nj1ZDBjvTYd22SMrLo/9E44TGfygppOM+vU2u8/sU7TYidY0CAAAA0GiEdAAAAAAA6ACsdaZsWbXGavVq6fyFhtXr11eaM9sJ5twyzBlVAmgPPPtflOvKiaSy4PTPMIoOGsTjMZo8SZo8yegfPu9MEbj0dastW6RItO46Bw9JBw9ZPf0zadZMq7vuNJoyiemw0DDhIYsVzewmV/n5RJl3x+8VuO1bKWwVAAAAgMYipAMAAAAAQDt26LDV6yusVq2SCs81rM7QIdKt84zmzJL69ePmMdqhSLD2KDoFYxQZMCdFDUJblpZmtHCBtHCB0aVLVitWOVNdHThY9/6RiLT2DWntG1bdukp33mF1xxKjHj3ob3Edbq9CY/9O/vU/ShR53nlZwdlflM3oksKGAQAAAGgMQjoAAAAAALQz589bLV8pvb7c6vCRhtUZNFCaf6vRrfOkvn24UYz2zbP3eblKTieVBWd8llF0cNO6dDF64APSAx8wOnLEaulyq9eXO9Ne1eX8BemZ30q/+Z3VpInO6DqzZ0o+H7+LqC085n75Nj0tEwlJkkwkJM+eZxWa+skUtwwAAABAQxHSAQAAAACgHSgrs1rzhhPM2bFTsrb+Ov37OcGc+bdK/RkxBx1FJCjfpp8mF/Ucr0i/mSlqENqrgQONPvWY0WOPWm3f4UyHtWatFAjU3tdaactWactWq5xO0pLbrO68w2jgAPpmVLMZXRQedqe8+55PlHl3/q9Ckz4mub2paxgAAACABiOkAwAAAABAGxUKWW3a4kyrsm6dFAzVX6d3b2nBrU44Z+AAyTByCDoYz9vPyVV6NqmMUXTQnNxuo8mTpMmTjJ74rDMd1suvWL17oO79r5RIf3pW+tOzViNHWL33PUbz50np6fyOQgqN/1BSSMdVfl6eQ8sVHnZH6hoFAAAAoMEI6QAAAAAA0IZYa7V3n7RsudWqVc7N3Pp06yotXCgtnG80ZDDBHHRg4YB8m36WVBTpNVGRPtNS1CB0NNnZRvfeLd17t9GBg1Yvv+JMh1VWXvf+e/dJe/dZ/ejH0m2LnMDO4EH04R1ZtPtIRXpOkPvM9kSZd8fvCekAAAAAbQQhHQAAAAAA2oATJ62Wr7Batlw6c6b+/TMzpVvnSosXGY0bK7lc3NQFvHv+IldZYVJZcMbnGEUHKTF0iNE/PGH06cedabBeesVq56669y0vl557XnrueasRw52wzoJbGV2nowqN/1BSSMd9ZodchW8rWjAqha0CAAAA0BCEdAAAAAAAaKXKyqxWrpZefc0ZPac+Ho80faoTzJkxXfL7uXkLJISq5N3886SicJ+pivSZkqIGAQ6/3+i2xdJti41OnLR65VWr15ZKl4vq3n/ffmnffqunfiItXmT13ruMhgymv+9IwoMXKppVkBQ69O74bwVu/24KWwUAAACgIQjpAAAAAADQikSjzkgKr7xqteYNKRCov87oUU4wZ/48KSeHG7VAXby7/leu8vNJZcHpn0lRa4C69e1j9PhjRh//mNW6DdKLL1lt3iJZW3vf8nLpb89Lf3veavhwq7vvMpp/q5SRwftAu+f2KjTug/K/9YNEkefd1xSc8yXZzK4pbBgAAACA+hDSAQAAAACgFSgstHp1qfTaUquzhfXv36ePdNsio0ULpV49uSELXFegVL7NP0sqCvedpmjvSSlqEHB9Ho/R3NnS3NlGZ89avfyq1cuvSpcu1b3//v3S/v1WP4qNrnP3XUZDhvDe0J6FRn9Avg0/kYkEJUkmGpJ35/8qOPNzKW4ZAAAAgOshpAMAAAAAQIoEAlZr35Beec1q+466R0qoKTdXWrjACefcMkwyhhuwQEP4tv5KpupKUllw5hdS1BqgcXr0MPr4x4weedhqw0bpxZetNm6q+z2jokJ6/gXp+Resht/iTIW1YD6j67RL6XkK33KXvHufSxR5d/2vglM+LnnTU9gwAAAAANdDSAcAAAAAgBZkrdX+d5zprFauksrKr7+/2y3NmC7debvRtKnO6AoAGs6UnZd32++SysJDFivaY0yKWgTcGI/HaPYsafYso8LC6tF1Ll6se//970j734mNrrPQ6r3vMRo2lPeQ9iQ08SNJIR1TVSzPvhcUHvt3KWwVAAAAgOshpAMAAAAAQAu4fNlq6evSq0utjh2rf/8B/aU77zC6bZGUl8dNVeBG+TY9LROuTKxb41Jg5udT2CLg5hUUGD36UaOPfNgZVefFl6w2XGN0ncpK6YWXpBdesho21Oru9xotZHSddiGaP0Th/rPlOfZmosy37TcKj7lfMq4UtgwAAADAtRDSAQAAAACgmYTDVus3OMGcDRukSPT6+2dlSgsXOqPmMJ0VcPNM0XF59vwlqSw88j7ZzgNT1CKgaXk8RrNmSrNmGhWes3rlVauXX5EuXGN0nXcPSP/+PaunfiItWuhMh3XLMN5r2rLQpEeSQjqu4uNyH16tyOAFKWwVAAAAgGshpAMAAAAAQBM7csTqlaVWry+Xioquv68x0qSJ0h23G82ZJfn93CwFmopv/Y9kouHEunX7FZz+6RS2CGg+Bd2NPvaI0cMPWW3c7Iyus3GTFK0jIFpZKb34krPP0KFWd7/HaNECRtdpiyJ9pinS9Ra5L7yTKPNte0aVhHQAAACAVomQDgAAAAAATaC01GrFKunV16z2v1P//j17SncsMVpym3NjFUDTchW+Le+7ryaVhcY/KJtdkKIWAS3D4zGaNUOaNcPo3HmrV16VXn7F6vyFuvc/cED6j+9b/fgn0sKFVnffZXTLLbwvtRnGKDTxEbmXfjlR5D69Ta6zuxTtMTaFDQMAAABQF0I6AAAAAADcoGjUatt26ZXXrN54UwoGr79/Wpo0b64Tzhk3VnK5uAkKNAtr5V/z7eQif7aCkz+eogYBqdG9m9FHPyI9/JC0cZP04stWGzZeY3SdKumll6WXXnZG13nvXc7oOpmZvFe1duFhtyv61pNylZ1LlHm3/UaBu36QwlYBAAAAqAshHQAAAAAAGun0GavXllq9tkw6d67+/UePcoI582/lZifQEjwHlsp9ZntSWXDKJ6T03NQ0CEgxt9to5gxp5gyj8+etXnlNeukVq/Pn697/wAHpe09a/eS/pIULrN77HqNbhknG8B7WKrm9Co3/kPxvfj9R5Dn4uoLFJ2Vz+6SwYQAAAACuRkgHAAAAAIAGqKy0Wvum9MqrVjt21r9/ly7SksVOOKdfP25qAi0mHJDvze8lFUVz+ig0/sMpahDQunTrZvTIw9KHPyRt3iK9+JLV+g1S5Fqj67ziBHoGDZJuv01atEDq0oX3tdYmNPp++TY+LROqkCQZG5Vv668UWPj11DYMAAAAQBJCOgAAAAAAXIO1Vrv3SK8utVq1WqqsvP7+Ho80a4Z0x+1GUyZLHg83MYGW5t3+W7lKziSVBeZ8SfL4UtQioHVyu42mT5OmTzO6cKF6dJ1rjRB3+LD04/+yevqn0pQpVksWG82aKfn9vNe1CmmdFBpzv3zbfpMo8ux9TsGpj8tmd09duwAAAAAkIaQDAAAAAMBVCgutlr4uvbbU6vSZ+vcfNEi683ajxQul3FxuVgKpYsrOy7fpZ0llkd6TFRm8KEUtAtqGrl2NPvJh6aEHpc1bY6PrrK97dJ1IVNqwUdqw0SorU7r1ViewM2Y002GlWmjiI/Lu/INMJCRJMpGQvNueUXDeV1LcMgAAAABxhHQAAAAAAFD1dFavLbXatr3+/bOzpcULnVFzhg7hxiTQGvjW/WdiqhdJsjIKzPuKxN8n0CBut9H0qdL0qUYXL8ZG13nZqvAao+uUlUsvvezs07OnM83jbYulXj35m0sFm9VN4ZH3ybv7T4ky7+4/KzjlE1JG5xS2DAAAAEAcIR0AAAAAQIfV2OmsXC5p0kTpzjuMZs1gig+gNXGd3S3P3r8llYVH3qtotxEpahHQtuXnGz38kDO6zvYd0tLXrdaulSqr6t7/zBnp17+x+vVvpLFjrBYtNLp1rpSTw3tlSwpOflSePX+RsRFJkglXyrf9dwrOeiK1DQMAAAAgiZAOAAAAAKADik9n9epSqzMNmM6qX1/p9iVGty1ypgQB0MpEw/Kv/LqMbKLIejMUnPn5FDYKaB9cLqNJE6VJE43+4fPOqHNLl1lt3yFZW3edXbulXbutfvCf0tQpVgsXOOHWjAzeQ5ubzemt8PC75N33QqLMu/MPCk76qJTWKYUtAwAAACAR0gEAAAAAdBDl5VZvvOmMBNCQ6ayyMqWFC5xwzojhTGcFtGbenf8r9/n9SWXBaY/LZnVLUYuA9ikjw+j226TbbzMqPGe1fIUzTeSJk3XvH4lI6zdI6zdYpaVJs2ZaLVpgNGWy5PXyvtpcglM+Ic++FxPBRRMsk3fX/yg09ZMpbhkAAAAAQjoAAAAAgHYrELDasFFasdJq/QYpGLr+/i6XNGWyE8xhOiugbTBl5+Vb/59JZZEugxWa8HCKWgR0DAXdjR56UPrQ30v733FG11mxSiopqXv/qippxUrnPTk7W5o902reXGeUHp+P99umZDsPVHjobfIeWJoo8237rULjH5J8mSlsGQAAAABCOgAAAACAdiUcdkbKWbHSmZKjoqL+Ov36Snfc7kxnlZ/PjUKgLfGt/Y5MsDypLLDgXyW3N0UtAjoWY5wR50YMN/rMp6w2bHLeg9etl4LBuuuUlkqvLnWmnczMlGbNcAI7UyYTkG0qoSmfSArpmKpieXf8XqGpj6WwVQAAAAAI6QAAAAAA2rxo1OrtvdLylVar10jFxfXXycpyprO6Y4nR8FuYzgpoi9zH1sn77mtJZaGR9ynae1KKWgR0bD6f0dzZ0tzZxplm8i0nsLN1qxSJ1l2nvFxatlxattwqPU2aMd0J7EybKqWn8958o6Ldhis8cJ48R9Ykynxbf63Q2L+T0nJS1zAAAACggyOkAwAAAABok6y1OnRIWr7KauUq6dy5+ut4PM50VrctMpo1k2/rA21aqFL+Vd9IKrJpOQrM+VKKGgSgpsxMo9tvk26/zaioyGr1Wmn5Cqs9b1+7TmWVtHK1tHK1ld8vTZ/qBHbuuN0qM5P37MYKzvhsUkjHBErk2/qMgrOeSFmbAAAAgI6OkA4AAAAAoE05dcpqxSrnm/nHjte/vzHS+HHSwgVG8+ZInTpxkw9oD3zr/lOu4hNJZYHZX5TS81LUIgDXkpdndN890n33GBWes3rjDWn1WmcUPGvrrhMISGvekNa8YfWt717WzBlezZoR1YzpUnY27+UNEe02QqGhS5KmvfJu/51C4z8km5mfwpYBAAAAHRchHQAAAABAq3fxotXK1U4wZ/87Dasz/BYnmDN/ntS1KzfzgPbEdXqbvNt/l1QW6Tle4VHvS1GLADRUQXej+z8g3f8Bo4sXrda+Ka1Za7VrtxS9xpRYwaC0ek1Iq9c4o+JNnGA1d47R7JlOAAjXFpzxWXkOvi5jnX9cE66Ud/PPFbz1/6S4ZQAAAEDHREgHAAAAANAqlZQ4U2OsWGm1c9e1v2lfU/9+TjBn4Xypd29u2gHtUqhSacv+j4yqOwXr9qlq8Tcl40phwwA0Vn6+0fvuld53rzMl1htvOYGd7dulyDUCO+GwtGmztGmz1feelMaMtpoz22jObCcAhGS280CFR9wj797nEmXe3X9UaOJHZDv1TGHLAAAAgI6JkA4AAAAAoNUor7BavSaoF1+MaNMW50Zcfbp3lxbOd8I5gwdJxnCDDmjPfOt+WGuaq+DMz8t2HpiiFgFoCnl5Rne/R7r7PUZXrli9uc4J7Gzddu3zgWhU2rlL2rnL6kc/lm4Z5oywM2eW1K8f5wNxwWmfkmf/SzLRkCTJRELybXxagcX/luKWAQAAAB0PIR0AAAAAQEqFQlabNjsj5qxbf1mVVfXXyc2V5s9zgjmjRkouFzfigI7Amebqv5PKIj3GKjTh4RS1CEBzyMkxuusO6a47jMrKpB07M/T6ioDWrQ8pGLx2vXfeld551+pnv5D697OaM1uaO8do6JCOHeK1Ob0UGnO/fDv/kCjz7P2bQhM+rGj+kBS2DAAAAOh4COkAAAAAAFpcJOJMYbV8hdWaN6SysvrrZGRIc2c7wZyJEySPp+PebAM6pECZ0pbWMc3Vbd+SXO4UNgxAc8rONnrve/x673v8On26SBs2Wq1902rDBl032HvsuPP43e+tCrpLc+c402KNGim53R3vHCI09TF53/6rTNj5RzM2It/a76jqvl9KHTjABAAAALQ0QjoAAAAAgBZhrdW+/c6IOavWSJcu1V/H55VmTHeCOdOnSX4/N5GADsla+Zd/Va4rTHMFdGSZmUYL5ksL5hsFAlZbtklvvGH11nqppOTa9QrPSX96VvrTs1ad86TZs5zAzoTxktfbMc4tbGZXhSZ+RL5NP02UeY6vl/vwKkUGL0hhywAAAICOhZAOAAAAAKBZHTlqtWKl1YpV0pkz9e/vdkuTJ0oLFhjNmeXckAPQsXl3/Y+8B5YmlUV6jGOaK6AD8/uNZs2QZs0wCoetdu2W1r5h9cZb0sWL1653uUh64SXphZessjKlmTOs5swxmjpZSktr3+ccwSkfl2fv3+QqO5co86/9rir6z5I8/hS2DAAAAOg4COkAAAAAAJrc2bNOKGfFKqvDhxtWZ8xo6b3vydRti3xyuUpkra2/EoB2z3XubfnWfjepzPpzVHXn95jmCoAkZwrMiROkiROMnvicM3LfG286U2peLyBcVi4tWy4tW27l90vTplrNne2M3ped3Q4DO94MBWd/SWmv/WOiyHXlpLzbf6fQlI+nsGEAAABAx0FIBwAAAADQJC5fdqaxWrHS6u29DaszZLAzldWC+VKPApdyc9MkScXFzdZMAG2Irbwi/0tPyERCSeVVS74t26lXiloFoDVzuYxGjZRGjTR6/DGrw0diI+y8KR0+cu16gYC09g1nX49HmjjBmRJr9kypc+f2E9gJ33KnIrv+V+4z2xNlvk0/VXjE3bJZ3VLYMgAAAKBjIKQDAAAAALhhZWVWa990gjnbtkvRaP11eveSFi5wwjn9+7Wfm14Ampa1VpG/fUGuK6eSyoMTP6rIoFtT1CoAbYkxRoMHSYMHGX3sEenkKSess/YNZ7SdawmHpU2bpU2brb73pDRmtDPCzpzZUkFBGz93MUaBW/+P0v/wARk5oxaaUIV8b35fgdu/W09lAAAAADeLkA4AAAAAoFECAat1G5xgzsaNUjBUf538fGnBfGnRfKNhw5ybZgBwPdGNv5Td/2pSWaTneAVnPZGaBgFo8/r0Nnrwg9KDHzS6cMHqjbecabF27pQi1wgaWyvt2i3t2m31o59Iw4dbLZxvNH+e1LVr2zyfiXYfqfCo++R9+6+JMu/+FxUe9T5F+kxJYcsAAACA9s9Ya22qG9GRFBUVpboJKWOMUW5uriSpuLhY/OoBHRt9AoA4+gOgbQiHrbZsk1ascG5oVVbWX6dTJ2neXGnhfKOxYyS3+/o3sugPAMS5C/co/U8PSjWmubJpuap46DnZ7B4pbBmAltYS5wfFxU4Aee0bVlu2SqEGBJCNkcaOkRbMN5o3V8rLbVuBHVN+URnP3C4TLEuURfP6q+Kh5yWPP3UNA66DzwsAaqJPABDXEv1BXl5ekx2LkXQAAAAAAHWKRq1273FGzFm9RrpSUn+d9DRp1iwnmDNlsuT1tq0bVgBagcpi+V9+IimgI0lVt3+XgA6AZpGba3Tn7dKdtxuVl1tt3CStfdNqwwapsqruOtZKO3dJO3dZ/fA/pYkTrRbMN5ozS8rObv3nPzYzX8GZT8i/+puJMlfRMfk2Pa3gzCdS1zAAAACgnSOkAwAAAABIsNbq0CFp+UqrFSul8xfqr+PxSNOmSosWGM2YLqWnt/4bUwBaKWuV9vo/y1VyJqk4OPnjigyYk6JGAehIMjONFsx3RsgJBJyRBN94w+qt9VLJNQLLkai0eYu0eYvVf3xfmjrFCezMmiFlZLTe86LQ2L+TZ/+LchfuTpR5t/xK4cGLFO0+MoUtAwAAANovQjoAAAAAAJ0+44Rylq+wOna8/v2NkSaMd4I5c+ZIndrAN8YBtH7ebb+R5/CqpLJIr0kKzvxciloEoCPz+52gzawZRuGw1dZt0spVztSf5eV11wmHpXXrpXXrrfx+ac5sq8WLjCZPlDyeVna+5HIrsOgbSv/D+2WiYUmSiYblX/oVVT74V8njS3EDAQAAgPaHkA4AAAAAdFBFRVarVjuj5ry9t2F1RgyXFi4wmn+rlN+lld1oAtCmuc7skO+tJ5MLM7socOf3JReXsACklsdjNG2qNG2q0ZcCVps2SytXW61bL1VdY0qsQEBavsIJQeflSQvnW9222GjYUMmY1nEeFe06TKHJH5dv09OJMvelQ/Kt/5GCc76UwpYBAAAA7RNXOAAAAACgA6mosHrzLen1FVZbtzrTM9RnQH8nmLNwvtSrV+u4oQSgnaksUtorX0yM5CBJMkbu9/9ENru7ZG3q2gYAV/H7jebMlubMNqqstFq/QVqxymrjJikUqrtOUZH07F+lZ/9q1b+ftHiRtHihVFCQ+nOr4LRPyn1kjdwX9ifKvFt/rUi/GYr0m5HClgEAAADtDyEdAAAAAGjnQiGrzVucYM5b65xvddene3dp4Xxp8SKjQQNTf/MIQDtmo0pb+v/JVXo2qdg15wm5Bs+TiotT0iwAaIj0dKMF86UF843Kypww9MrVVlu2XDsMfey49PNfWv38l9K4sVa3LTKaN1fKTtX0oW6fAku+o/T/eb9MxEkZGVn5X/uyKh/6m2xmfmraBQAAALRDhHQAAAAAoB2KRq32vO1Mr7BqjVRSUn+dnE7SrbdKixYYjR4luVyEcwA0P+/WX8tzdG1SWaTPFHnmM80KgLYlK8vo9iXS7UuMioqsVq6Wlr1utf+da9fZuUvaucvqB/8pzZzpBHamTpG83pY9D4t2HargrH+Qf+13E2Wuiovyv/aPqrrvF0w7CAAAADQRzqwBAAAAoB05fMTq9eVWK1ZJ587Vv39amjRrprR4odHkSS1/QwhAx+Y6vU2+t36YVBbNyFfgju8pzeVOTaMAoAnk5Rm9/z7p/fcZnThhtWy51evLpbOFde8fDEmr10ir11jldJIWzLdacpvR8FskY1rm/Cw04cNyn9ggz9E3EmWeExvle+sHCs75xxZpAwAAANDeEdIBAAAAgDausNBq+Upn1JwjR+vf3+2SJk92gjmzZkoZGQRzAKRAxWWlvfJFGRtJFFkZBe74d9msbilsGAA0rb59jT7+MaOPPeKMdLhsudWq1VJZWd37XymRnnteeu55q/79pNuXSLctkvLzm/mczbhUteQ7yvjdPXKVn08U+7b+WtFuIxS+5c7mfX0AAACgAyCkAwAAAABt0JUrVqvXSK+vsNq9p2F1Ro+SFi00unWelJdLMAdACtmo0l77slxlyUN+Bad/WpG+00UPBaA9crmMxo6Rxo4xeuKzVhs2Sktfd57D4brrHDsuPf0zq5/9Qpoy2er2JUazZkh+fzP1lOl5qnrPD5X+54dloqFEsX/ZPyvaqZeiPcc1z+sCAAAAHQQhHQAAAABoI6qqrN5a5wRzNm2WIpH66/Tv74yYs3CB1LMHt70BtA7ezb+Q5/hbSWXhvtMVmvrJFLUIAFqWz2c0d440d45RSYkzss6y5c5IO3WJRqWNm6SNm6yysqSFC6zuWNI802FFe45XYP4/K23F1xNlJhJQ2gufVuUH/1c2t2+Tvh4AAADQkRDSAQAAAIBWLBy22rrNmcrqjTelyqr663TrKi1cKC1aYDR4UNPfuAGAm+E6uVm+9T9KKotm5itw+79LLneKWgUAqdOpk9E9d0v33G10+ozV68ul15ZZnTlT9/5lZdLzL0jPv2DVr68zHdaSxU07HVZ49P0Knd8v7+4/JcpclZeV/tdHVXn/f8tmd2+y1wIAAAA6EmOttaluREdSVFSU6iakjDFGubm5kqTi4mLxqwd0bPQJAOLoD4DarLXau88J5qxcLRUX118nO1u6dZ4TzBk7xplOoa2hPwDaP1N+Uem/v0+u8guJMmtcqnr/M4r0mVK9H/0BgJiO2h9Y60xp+upSZ5Sdysrr7+9ySZMnSXcsMZo1s4mmw4qElPb84/IcX5dUHM3rr8oP/FY2q9vNvwbQCB21PwBQN/oEAHEt0R/k5eU12bEYSQcAAAAAWonjx61eX2G1fKWu+c3pmnw+adZMZzqrKZOdaRMAoNWKRuR/7Z+SAjqSFJz+maSADgDAudEwdow0dozRE5+1Wvum9NpSq23b694/GpU2bZY2bY5NhzXf6vYlRiOG38Soim6vqu76odL/9CG5L76bKHYVHVPaXz+qyg/8TsrofGPHBgAAADooQjoAAAAAkEIXLlitWCUtX2l14ED9+7tc0qSJzog5c2ZLmZkEcwC0Dd5NP5XnxIaksnC/mQpNfSxFLQKAtiE93WjJYmnJYqPCQqulrzsj7Fx3OqwXpedfrJ4O67ZFUteuN3De6M9S1ft+qfRnH5br8pFEsfvSYaX/5aOq/MAzUnrTfasYAAAAaO+Y7qqFMd1VriSGnQNAnwCgGv0BOqLSUqu1b0ivr7DasVNqyK/98OFOMGfBrVKXLu0zmEN/ALRf7hMblfaXj8qo+u86mtlNFQ/9rc5RGOgPAMTRH9QtPh3Wa0ud6VEbMh3WpInOdFizZzV+OixTdl7pf35IruITSeWRLkNU9b5fMvUVWgT9AYCa6BMAxDHdFQAAAACglqoqq/UbpRUrrTZslEKh+uv07u1MZbVoodSnd/sM5gBo/0zpOflf/VJSQMcat6ru/D7TpADADao5HdbnP2v1xpvO6Drbd9QdAI9Gpc1bpM1brLIypfnzrZYsNho9qmHTYdmsbqr8wG+U/ucPy3XlVKLcfemg0v/496p83y9l8/o34U8IAAAAtE+EdAAAAACgmQQCVhs3S6tWWa3bIFVV1V+nS2dpwXwnnDNsWMNumgBAqxUJKe2Vf5Cr4lJScXDm5xXtPSlFjQKA9iU93ei2xdJtNabDem2p1elrTYdVLr34kvTiS1a9esqpu0jq1ev65502u4cq3/8bZ0Sd0rOJclfJaaX/6UOquu/ninYb0ZQ/GgAAANDuMN1VC2O6q1xJDDsHgD4BQDX6A7Q3oZDV5i3SqtVWb66TKirqr5ORIc2bIy1aaDRhvOR2d8xgDv0B0P741nxHvu2/TSoLD5ijqnuelozrmvXoDwDE0R/cmJrTYa1a07Bz0tGjpCW3Gd06T+qUfe3zUVNyWul/fVSuomPJr+nLVNV7f6JI36k303TgmugPANREnwAgrq1Nd0VIp4UR0smVxJslAPoEANXoD9AehMNW27ZLK1c7Uw2UldVfx+uVpk11RsyZMV3y+ztmMKcm+gOgfXEfWKb0l59IKot26qWKB/8ipedety79AYA4+oObV1npnKO+tsw5Z63vn9DnlWbMcAI706ZIHk/t81RTcUlpz31C7vP7ksqt26uq2/9dkaFLmvJHACTRHwBIRp8AIK6thXSY7goAAAAAbkA4bLV9h7TmDau1a6UrJfXXcbmkCeOl+bcazZt7/W8oA0BbZi4fVdqy/5NUZt1eVb3nP+sN6AAAmlbSdFjnrJavcEbYOXGy7v2DIWnNWmnNWqvcXGnhAqsli42GDa2eitVmdFHlB36rtBc/K8/JjYm6JhJS2sv/oOCtlxQa/2AL/HQAAABA20JIBwAAAAAaKBi02rJVWvuGM5VVaWn9dYyRxo6RFsw3mjdHyssjmAOgnQtVKO2lz8mEkudWCdz6L4p2H5miRgEAJKmgu9FDD0of+nvpnXelpcusVqy8duC8uFj6y1+lv/zVqn9/6bZF0uJFUvduRvJnqerenyrt1X+U59DyRB0jK//qb8qUX1Bw5uedE2IAAAAAkgjpAAAAAMB1VVVZbdosrV5rtX6DVFFRfx1JGj3KGTHn1rlSfj43JgB0ENbKv+Lrcl86lFQcGnGPwqM/kKJGAQCuZozR8Fuk4bcYfeZTVhs3Sctet1q3QQqF6q5z7Jj0s19Y/fyX0tgx1gmhz/Up764fyL/q3+Td/aek/X2bfyZTfkGBRf9XcnErAgAAAJAI6QAAAABALeXlVus3SmvXWm3cLFVVNaze8FtiwZx5zreUAaCj8e78H3n3v5RUFskfpsCCrzGSAgC0Ul6v0exZ0uxZRiUlVqtWS0tft3p7b937Wyvt3CXt3GX1w/+UJk40WjT/a1oyqZuytz6VfOy9z8lUXFLVXT+QvOkt8NMAAAAArRshHQAAAACQdPGi1VvrpXXrrbZtk4LX+Abx1YYPl+bNMZo3V+rVkxvQADou97G35Fvz7aQy68tS1Xt+yI1ZAGgjOnUyuudu6Z67jU6esnp9udXSZdLZwrr3j0SlzVucx394P6kvzMnX32X+XxlFE/t4jq5V+t8eU+U9T0u+zBb6SQAAAIDWiZAOAAAAgA7JWqvDR6S31jnBnP3vNKyeMc5UVvPmGs2dI3XvRjAHAMylQ0p7+QsyNpJUXnXbt2Tz+qemUQCAm9Knt9HHHjF65GGrPW9LS5dZrV4jlZXXvX8wJH135fu1sXtn/fukL8nvDiS2uU9tUfpzn1DlvT+T/Fkt8wMAAAAArRAhHQAAAAAdRjhstXOX9NZ6q3Xrrv2N4Ku5XdK4cU4wZ/YsKb8LwRwASKi4rPTnH5cJliUVB6c+rsiQRSlqFACgqbhcRmPHSGPHGD3xOatNm6WVq5xRKAOB2vuvPTdfn1j/K/1o6qeU4ytJlLvPbJf/L48q8L6fS2mdWvAnAAAAAFoPQjoAAAAA2rXSUquNm6W31llt2nTtb/5ezeORJk10prKaNVPKzSWYAwC1hINKf+lzcl05lVQcGrpEwRmfSVGjAADNxe83mjNbmjPbqKLCat0GJ7CzcZMUDlfvt6tovD6+/hn9dPqj6uwvSpR7z+3S2R99VLvG/FKTZ+UoK4tzbAAAAHQshHQAAAAAtDtnzlqtW+8Ec3bukiKR+utIUlamNG2aNGuG0dQpUnY2Nw0A4JqiYaW99k9yn96WVBzpPlqB274tGVeKGgYAaAkZGUaLFkiLFhiVlFq98aa0YqXV9h1SNCodKLlFH1//G/1s+keVn3YpUa+vZ6+K3vqkPvCDX2jkuEzNm2M0c6aURygeAAAAHQAhHQAAAABtXjBotWu3tGGj8y3eEycbXreguzRrlhPMGTtG8nq5OQAA9bJR+V//qjwHlyUVR7MKVHX3jyVvWooaBgBIhU7ZRnfdId11h9Hly1ar1zqBnT1vD9aj63+jn8/4qLqlXUjsP7bzLn1vwmf12U1Pa+Mmv1zfl8aNtZo722jGdKlHD87JAQAA0D4Za61NdSM6kqKiovp3aqeMMcrNzZUkFRcXi189oGOjTwAQR3+AG3XmrBPI2bjJ+bZuVVXD6w6/RZo5w5nGatBA5/cQqUd/ALQR1sq36pvy7fqf5GJvhiof+G9Fu4246ZegPwAQR3/Qtl28aLX2TemdDcf0RM4j6p5+Lmn76rPz9Y9bn1TYepPKBw6Qpk+TZkw3GjlC8ng4Xwf9AYBk9AkA4lqiP8jLy2uyYzGSDgAAAIA2IRBwRsvZuKnxo+X4vNLEibFgzgwpP5+L/ABwo3xv/aB2QMftU9XdP2mSgA4AoP3Izzd6372S7h2gsmO/UtULDyktUv1F1lt7rNLXx31VX93xLVlVT5N45Kjz+MP/WnXqJE2bYjV9ujMlbSempAUAAEAbRkgHAAAAQKt1+owTyNm0yWr7zsaNlpOb43z7dtYso8kTpYwMLuYDwE2xVr43vyff1l8nF7s8qnrPfyrSd1qKGgYAaAuy+g9S9O9+IfvsR2SCZYnyu/q8pPJwpr69518k1T5nLymRXl8hvb7Cyu2SRo+2mjHdaPo0qX8/RsUEAABA20JIBwAAAECrUV5utWOntHWb1eYtjRstxxhnGqtpU50L9sOGSi4XF+wBoElEQvIv/5q8+55PKrbGpcDt/67IwHkpaRYAoG2Jdh+pynt/qvS/PioTrk7gPzDgj+raJ1v/960ndKXk2vUjUWnnLmnnLqv/+qlU0F2aPNlqyiSjiRMZZQcAAACtHyEdAAAAACkTDlvt2y9t2Wq1dZu0b59z4b2hcnOlqZOlqVONpkyScnO5KA8ATS5UqbSXvyDP0bW1NgUWf1PhYbenoFEAgLYq2muiqt7zI6W98GmZaChRPt/zC03/l07alfkxrdtgtX6DdPjw9Y9VeE566WXppZetXC5p+C1WUyZLkycZjRgueTx8PgAAAEDrQkgHAAAAQIux1urECWnLNme0nO07pIqKhtc3Rhox3BktZ9pURssBgOZmKi4p7YXPyH12Z1K5NW4noDPynpS0CwDQtkUGzFbVHf+htFf+QcZWp/TT3/q+xi3urFGP3qfHHpUKz1lt2Cht2GC1dbsUDF77mNGotHef83jmt1aZmdKE8VZTJjuB/l69+NwAAACA1COkAwAAAKBZFRVZJ5QTGy3n/IXG1c/NlaZOcYI5UyZJOTlcXAeAluAq3KO0Fz8nV1lhUrn1pKnqrh8wxRUA4KZEht6mQPDflPb6PyeV+5d/VTYtR5HBC1TQ3ejeu6V77zaqqrLatl1aHxtl58LF6x+/vFx68y3pzbesJKlnT6uJ46Xx440mjJfyu/C5AgAAAC2PkA4AAACAJlVebrVrt7RthxPKqW+I+qt5PNLoUc4Q9ZMnMVoOAKSCZ+/f5F/xdZlI8pAF1t9Jlff+VNGe41PUMgBAexIedZ8CVVfkf+PfE2XGRpX2yj+o8r5fKNpnSqI8Lc1o5gxp5gwja62OHJU2b3Gmzt256/qj7EjSmTPO46VXnNBO3z5WE+KhnXFSXh6fOQAAAND8COkAAAAAuCmVlVZ73pa277DatkM68K4UidZfr6ZBg6RJE6XJE43GjpHS07lADgApEQ7Kt/Y78u3631qbolkFqrrvZ4rmD01BwwAA7VVo0iMylZfl2/LLRJmJBJX+wqdUef/vFO02olYdY4wGDZQGDZQ++IBRIGC1e4+0eYvV5q0N+6LAiZPO4/kXndDOwAHVoZ3xY6VOnfhMAgAAgKZHSAcAAABAowQCVnv3xUI526X970jhcOOOkZ8vTZ7ojJYzcYLUhaHmASDlzKXDSnv1S3JfeKfWtkivSap6zw9lM7qkoGUAgPYuOOsfZCqL5H37r4kyEyxX2nOfUOUDv5fN63/d+n6/Mwrn5ElGn5Z08ZIzqueWWGinqKj+Nhw56jz+8pyVMdLgQdWhnXFjpKwsPrMAAADg5hHSAQAAAHBdoZDVvv3Sjp1OMOftt6VgqHHHSE+Xxo+rnsKqX1/n268AgFbAWnne/ov8q78lE66qtTk4/iEF5/yj5PamoHEAgA7BGAUWfl2m6oo8h1Ykil0Vl5T+10edoE52QYMPl9/FaMliaclio2jU6vARafsO5/PMzl1Sefn161srHTzkPP70rJXLJQ0ZbDV+nDRunNHY0VJ2Np9nAAAA0HjGWmtT3YiOpKghkf12yhij3NxcSVJxcbH41QM6NvoEAHH0B61PIOCEcnbtlnbvcYaNr6p9z/a6vF5p5AgnmDNpotHIEZLHw0VsXB/9AZACVVeUtvxf5Tm4rNYm6/YrsOjrCo+4p8WbRX8AII7+oIMJB5T2t8fkObkpqTia20+V9/9ONqvbzb9E2OrgoerQzu7dUmUjP+84I+1I48ZK48c5U/bm5PB5p7nRHwCoiT4BQFxL9Ad5eXlNdixCOi2MkE6uJN4sAdAnAKhGf5B6JSVWe96Wdu1xLlC/827jp69yu6Thw6Xx46WJ441GjZTS0rhIjcahPwBaluv0dqW9+iW5Ss/W2hbpMliBO76vaNehKWgZ/QGAavQHHVCgTOnPPiz3+X1JxdHOA1X5gd/KZuY36cuFw1bvvFsd2tnzthQINP44gwY6oZ1xY43GjZXy8vg81NToDwDURJ8AII6QDq6LkE6uJN4sAdAnAKhGf9Dyzp13RsfZtdsJ5Rw52vhjOMO9SxMnSOPHO8O9Z2RwERo3h/4AaCHRsHwbfyrvpqdlbLTW5tDYv1Ngzpclb1oKGuegPwAQR3/QMZmKS0r/84flunwkqTzSZYgq7/+tlN50N0muFgxa7X/HCe1s2261d58UauR0v5LUv39spJ1YaKdLFz4v3Sz6AwA10ScAiCOkg+sipJMriTdLAPQJAKrRHzSvaNTq+Aln6qo9e6x27ZYKz93YsQYNkiaMkyZOcIZzz87mIjOaFv0B0PxMyWmlvfpPcp/ZXmubTctR1eJvKjJ4YQpaloz+AEAc/UHHZcrOO0Gd4uNJ5ZGut6jyfb+SMjq3SDsCASeos2On1c5d0t69UvAGQjt9+jjTAY8bazR+rNS1K5+nGov+AEBN9AkA4tpaSMfTZEcCAAAAkHJlZVb79kt790lv73WWS0sbfxyXSxo8WBozWho72mj8OCk3l4vIANCWed59Tf4V/yoTqP3GEO49RYHbvyubXZCClgEAUJvN6qbKD/zGCepcOZkod194Rxl//rAq3/cr2ezuzd4Ov99ownhpwnjn81Ag4Iy0s3OXE9x5e2/Dpsc6edJ5vPiSc9OoV0+rceOk8eOckXYKuvN5CwAAoCNgJJ0Wxkg6uZJItAKgTwBQjf7gxsVHydm71wnk7N0nHTsu3cg/oc8njRgeC+WMMRo1UsrM5CIxWhb9AdBMguXyr/6WvHufq7XJGreCMz6r0ORHJZc7BY2rG/0BgDj6A5iS00r/00NylZ5NKo/m9Fbl+34tm9snRS1zhEJW77wr7dgp7dxltWePVFnV+OP0KHCmxxo3zvmSRI8C5/cf1egPANREnwAgjpF0AAAAADSLy5edb2y+864TyNm3Tyorv7FjZWVVB3LGjJaGDZV8Pi4AA0B74zq3V2mvfLHWVCGSFM3po6o7/kPRHmNT0DIAABrGduqlyg/8VunPPpwU1HFdOaX0Pz2oqvf9StH8ISlrn9drNHqUNHqU9OEPGYXDVu8eqB5pZ/ceqaKi/uOcLXQery1zbip16yaNH2s1bqzRuHFS716EdgAAANoDRtJpYYykkyuJRCsA+gQA1egP6lZS4lzYjYdy3nlHOn/hxo/Xras0ZowTyhk7WurfX3K5uMCL1oX+AGhCNirvtt/I99YPZaKhWptDw9+jwPyvSf6sFDSufvQHAOLoDxBnSs8q/S8fk6voaFK5TctR5Xt/rGjvSSlq2fWFw1aHDkk7djkj7ezadWNftsjPj420M9Zo/Fipb9+OF9qhPwBQE30CgLi2NpIOIZ0WRkgnVxJvlgDoEwBUoz+QKiqsDhxMDuScPnPjx/N6paFDpFEjpREjjEaOkAq6d6yLt2ib6A+ApmHKzsu/9CvynNhQa5v1ZSow/2sKj3hvClrWcPQHAOLoD1CTqbiktL9+XO4L+5PKrdurwOJvKTz8rhS1rOEiEavDR6SdO6Udu6x27ZZKShp/nM550tix0vhxRuPGSgP6t//QDv0BgJroEwDEtbWQDtNdAQAAAC3o8mUnkHPwkHTgoNXBg9Kp0zd3zG7dnEDOyFggZ+gQpq4CgI7KfWS10pb9s0xl7S8JRQrGqOqO/5DN7ZuClgEAcPNsRhdVfuA3Sn/+cbnPbE+Um0hIaa/9owJXTio09ZNSKw6ruN1GQ4c4n9vu/4BRNGp19Ji0Y6e0c6fVzt1ScXH9x7lcJK1eI61e49yEys2RxsanxxorDRrI6KkAAACtESEdAAAAoBlEo1Znz0oHDkoHDjlhnIMHpUuXb+646WnSsGHSLcOkUSOdUE7Xrlx4BYAOLxyQ743/kG/nH2ptsjIKTfmEgtM/Lbm9KWgcAABNKK2TKt/3C6W98kV5jqxJ2uRf/yO5io8rsPD/Sh5/atrXSC6X0aCBTqjm/fcZWWt17Li0c5e0Y6fVzp1OIKc+xVektW9Ia99wQjudOkljR1uNi420M3iQExACAABAahHSAQAAAG5SMGh1/IQTwokHcg4dlsrLb+64Pq80eLATyBl+i9Ett0h9+3BhFQCQzHXxoPyvfFHuSwdrbYtmdVdgyXcV6Ts1BS0DAKCZeDNU9d4fy7fmO/Lt/H3ypn0vyHXpsKre85+ynXqmqIE3zhijAf2d6avuvdsJ7Zw8Ke3YJe3c5YR2Llys/zglJdKb66Q31zmhnaxMacwYZ6Sd8eOkIYMlj4fPlgAAAC2NkA4AAADQQNZanTsvHT4sHTkqHTpsdeSIdOKkFInc3LHdLmngQCeQc8stRsNvcS7Ker1cNAUAXIONyrvjD/K9+T2ZSLDW5vCgBapa/G9SetPNmw4AQKvhcis4/59lc/vIt+Y7MrKJTe5zbyvjD+9X1Z3fV6Tv9BQ28uYZY9S3r9S3r3T3e5zQzunTsZF2dlnt3CWdO1f/ccrKpfUbpPUbnH+njAxp9Cir8bGRdm4ZRmgHAACgJRDSAQAAAOpQVmZ15Kh0+Ih0+IgTxjlyxLmwebPS0pyhxocMkYYMNho6WBowQPL7uSAKAGgYU3Ze/mX/LM/xt2pts540BeZ9ReHR90uG9xYAQPsWmvBhRTv1Utpr/yQTqkiUm8oipf31UQVnfl6hyY9KxpXCVjYdY4x695Z695buutN5nz971iamx9qxSzp7tv7jVFRImzZLmzY7oZ30NGlULLQzYTyhHQAAgOZCSAcAAAAdWjhsdfKUE8A5dMQ6o+QckQob8E3EhsjNiYVxhkhDBxsNGexcTGXKKgDAjXIffF1py/9Vpqq41rZI/jBV3fk92S6DW75hAACkSGTwAlV+8I9Ke/GzchUfT5QbG5X/rR/IfXyDAku+LZtdkMJWNp8ePYx69JBuX+J8zjx33pkWa+cuqx07pVOn6z9GZZW0Zau0ZWt1aGf06OrQzrChhHYAAACagrHW2vp3Q1MpKipKdRNSxhij3NxcSVJxcbH41QM6NvoEAHEt1R9Ya3XpsjNVVc3RcY4fl4KhpnmNHgWxMM4QJ4wzdIiUn+/8jADqx/kBUI9gufyrvyXv3ufq3jzhwwrO+gfJ42/hhjU9+gMAcfQHaJRAqdKWfkWew6tqbbL+HFUt/oYiQxanoGGpdfGiM8LOzp3OiDvHTzT+GOnp0tgxSoR2hgxu+dAO/QGAmugTAMS1RH+Ql9d0U4kzkg4AAADancpKq6PHqkfHOXLECedcKWma42dlSoMGSQMHSoMGGGd5gJSZSRgHANA83EfWyr/y/8pVWnv+imhmNwWWfEuRfjNT0DIAAFoRf7aq3vuUvJt/Lt+6H8mo+gaNCVxR+kufV2jEPQrM/ScpvelutLR2+flGixZIixY4n1kvXbLaudsZaWfnTunosfqPUVkpbdwkbdzk/JtmZkpjR1uNqxHaYcRYAACA+hHSAQAAQJsViVidORsL4xy2OnLUCeOcPiM1RVje7Zb69Y2FcQYaDRrohHO6dWV0HABAyzDlF+Vb8y15332tzu3hIYtVtfDrHepGIwAA12VcCk39pCI9JyjttS/LVVaYtNm773l5jq5VYO6XFR7+XqkDfrbr0sVowa3Sgludn72o2Gr3bmnHTqvtO6QjR+s/Rnm5tH6jtH6j8+E7K1MaO9Zqwnij8eOkwYMkl6vj/dsCAADUh+muWhjTXeVKYtg5APQJAKo1tD8oKoqFcI5IR45YHT7ifNuvqqpp2tE1PxbGGVQ9Ok6/vpLXy0VFoKVwfgDUEA3Ls+dZ+d/6T5nAlVqbrTdDgflfVXjE3e3y5iL9AYA4+gPclKor8q/4urwHlta5OdxnmgILvirbeWALN6x1Kyp2RtjZsdNq+07p2LHGHyM725kea8xoo7FjnOmgb/bzNf0BgJroEwDEMd0VAAAAcBPKymJTVR2Vjh51gjlHjkrFxU1z/PQ0J4wzcKA0eKCJjZIjderU/m5wAgDaJvexdfKt/Y7clw7VuT3cZ6oCi/5NNrdPC7cMAIA2Ji1HgTufVGTAXPlXf1MmWJ602XNyo9y/fa/Co+5TcNqnZbO7p6ihrUtertGt86Rb5zmfky9fttqxywnt7NghHT9R/zFKS6W31klvrXNukvn90sgRVmNGS2PHGI0cIWVk8DkcAAB0PIyk08IYSSdXEolWAPQJAKRAwOrYcenoUen0Wb8OHYrowIGQzp1vmuO7XFLvXrEwziCjgQOcUXJ6FDDkNtBacX6Ajs514V353vqBPEfX1rnd+nMUmPtPCo+8t12OnlMT/QGAOPoDNBVTek7+Nd+S5+DrdW63njSFxj+k4KRHmEayHpcuWe3YWT3SzsmTjT+G2yUNHqxEaGfMaKlz5+uf39AfAKiJPgFAXFsbSYeQTgsjpJMriTdLAPQJQEcSDludPFV7ZJwzZ6RotGleIy9PGjjAmfN+4ECjQQOlAf0lv79938AE2hvOD9BRuc7vk2/j0/IcWnHNfULD7lBw3v8nm5nfgi1LHfoDAHH0B2hq7sOr5V/1b3KVnq1zu/WkKTTyXoUmPCyb16+FW9c2XbzohHXiI+2cOn1jx+nRQxo5Qhox3GjEcGeKLJ+v+nM9/QGAmugTAMQR0sF1EdLJlcSbJQD6BKA9CoetzpyVjh9XbLoqq6NHnWGww+GmeQ2/X+rXTxo8UBoUHx1nYP3ftgPQNnB+gA7FWrlOb5Vv6zPyHFl9zd0iXW9RcO5XFOk7tQUbl3r0BwDi6A/QLILl8m3+hbzbfysTrqpzFyujyOAFCo15QJG+0yWXu4Ub2XadP2+1c5e0a7fV7j3ONYIb4fFIQwZLI4Y7wZ1RI41GjsyVMYb+AADnCAASCOngugjp5ErizRIAfQLQlgUCVidOOmGc4yecKauOHXO+KRcKNc1ruN1S3z7O6DgDBjhhnIEDnG/Vud0EcoD2ivMDdAihKnnefUXeHb+X+8I719wtmpGv4MzPO1NbdcCbgvQHAOLoD9CcTNl5+Tb+lzx7/iJjI9fcL5rVXeERdys08h7ZvAEt2ML24coVqz1vS7v3WO3aLb3zrhS59j/3deXmGo0e5dGQwWENGeyEeLp3d/oKAB0L5wgA4gjp4LoI6eRK4s0SAH0C0BaUlTkBnOMnpOPHY2Gc49LZs1JT/ckaI/XsKQ0b6tXgwR716lGlAf2lPn0kr5cLbEBHw/kB2i1r5Tq/T569z8v7zksyVVeuvas/R8EJDyk08SOSL7Pl2tjK0B8AiKM/QEswRUfl2/C0PAdek4lefyjYSNdbFB68UJEhixTtMsT5YItGqaqy2rdf2r3HCe7seVuqrLzx42VlKRHYGTLYaPBgqX8/risA7R3nCADiCOngugjp5ErizRIAfQLQWgQCVqfPSKdOOSPhnDxldeqUdOKkdOlS075W13xpQGxEnIGx0XH69ZMyMlz0BwAkcX6A9scUHZfn0HJ59r0g96VD193XpuUqOOkRhcb+veTPaqEWtl70BwDi6A/Qkkxpobw7/lve3X+WCZbVu380t5/CQxYpPHihogWjJeNqgVa2P+GwM1323v3Svv1OgOf48Zv7gpDHIw3o7wR3Bg82GjTQCe507syoO0B7wTkCgDhCOrguQjq5knizBECfALSkUMjq7Fnp5FVBnFOnpHPnm25UnLjcXOfCVzyMM2CAE87plF33RTD6AwBx9Ado86yV69xeeQ6vkPvQynqDOZIUzemr0Pi/V2jU+zv0yDlXoz8AEEd/gJQIlMm77wV59j4n9/l9DaoSzcxXZMA8hQfNV6TvNMmb3rxtbOfKyqzeeVfat1/au88J7jTF7ZXsbOeaRf9+Uv/+Rv37OV8g6tZVcrkI7wBtCecIAOII6eC6COnkSuLNEgB9AtDUSkutzhY6U1GdOSsVFlqdOu0EcQoLpUi06V+ze3epX9/qC1vx5Zycxl3Uoj8AEEd/gDapskjuk5vlObFB7iNr5Co716Bq4X4zFRr/IUUGzOFb93WgPwAQR3+AVHNdeFeevX+T552X5apo2JCz1pOmSN/pCg+6VZGB82QzuzZzK9s/a63OnTM6djxDu/eE9fbeKh04KJXVP+BRg/i8znTcvXpJvXtJPXsa9e4l9eopFRRIHg8BHqC14RwBQBwhHVwXIZ1cSbxZAqBPABqrqsoJ4RQWOiGcs2eTQzlNdVHqam6X1LNX9bfM+vUz6t9X6ttXyshomgtU9AcA4ugP0CYEyuQ+vU3ukxvlPrFJ7gv7G1w12qmXwiPuVmjE3bK5fZuxkW0f/QGAOPoDtBrRiFxndshz8HV5Dq2Qq/Rsg6tGCsYoPHCeIoPmK5o/VGK6pRtydX8QjUZ17rx08KB08JB08JDVoUPS2cKmfV23S+peEA/vSD17GHXrKnXr5jzyuxDiAVKBcwQAcW0tpONpsiMBAADghlhrVVzsTD114YJ0/rx07oLV+fNOCOfsWelyM+d8u3SWeveOPXoZ9entBHF695J8Pi40AQA6qGhErsuH5Tq7W+6zu+Q6u0uuS4dk1PCLPdGMfEUG3arQLXcp2nsSo+YAANBWudyK9p6kYO9JCs77/5wpLg8tl+fgcrmKjl63qrtwt9yFu6X1P1I0u4fCgxYoMmSRIr0mSi53C/0A7Y8xRgXdpYLu0uxZkuRcvygttTp02AnuHDrkLB8/IQUCN/Y6kah05ozzcCSfC7pcUn4XmwjtOAEeo+6x9S6dpc6dCfIAAABHSkI6xcXF2r59uwoLC1VWVqZu3bqpd+/eGj9+vNzulj8hLS8v19atW3Xu3DkVFxerc+fO6tWrlyZOnCifz9fi7QEAAO2HtVYlJdL5WPjGebY1lp1gTjDU/G3JzakdxOndW+rTu+lGxQEAoM0KVch18ZBcF9+V68K7cl94V64L+2WC5Y0+VDS3n8KDFyo8eIGiPcYSzAEAoL0xRtGCUQoWjFJw1hdkio7Kc3iNPEdWy3V6u4yNXLOqq/SsfDt/L+38vaLpnRUZvFDhIYsV6TNFcntb8Idov7KzjcaPk8aPk+LBnWjUqvCcdOyYdOy4dOy4TSxXVNzc60WjsWs8FyTtjZfWDnXn5lh1jgV2unSWunSROnc2yet5UlaW5HJxnQYAgPaqRUM6R48e1fe//32tWbNGoVDtO1Fdu3bVAw88oMcee6xFwjHnz5/Xk08+qWXLlqmijrOwnJwcvfe979UTTzyhrKysZm8PAABoWyoqrC5eki5eVOL50mXrPF+qLquqapn2GCN1zZd69Ig9CqQ+vU0imNMpmws8AAAoEpK5clKuS4fkunQoFsZ5V6b4RKNGyKnJetIV6TVRkb5TFR44T7bzIKaxAACgA7F5AxSaNEChSY9IlcXyHHtT7sOr5Dn25nUDv67Ky3Lt+bO8e/4s689ReNCtTmCn3wzJ42/Bn6D9c7mMevaQevaQZkyX4uEda60uXZZOn449zlidOu2MmnPqtFRa2nRtKL7iPI4kDbxU+/zT7ZI65Vjl5kg5OVJubuyRI+XmmqTyvFxn2evl3BMAgLbC2BaaoO+FF17Q17/+9TrDMFcbMWKEnnrqKfXu3bvZ2vPWW2/pS1/6koqK6p87ok+fPnrqqac0fPjwm37dhrxee8XckABqok9Aa1ZR4VygiYdvLl2SLl60NYI4znNlZcu3LTfXCeD0LIiHcZyLTAUFUvdubXNqKvoDAHH0B2hSkaBcRcdjYZzDMpcPO8tFx2WiNzeEnXV5Fe0xRpE+0xTuO03RHmMkNyPxNiX6AwBx9Ado0yJBuU9tlfvwanmOrJKr5Ez9dSRZb4bCA+c6gZ0BcyRvRjM3tG1IRX9QUmJ1+owT4HHCO1bnzjtTlp8/LwWDzd6EBsnMjAV3cmoGeqROnYw6dZJyOknZ2c4+nbKlTp0kv7/tXUMCauIcAUBcS/QHeXl5TXasFgnprF27Vp/85CcVjUYTZf3799fUqVOVm5urEydOaPXq1aqq8TXzwYMH609/+lOzjGCzd+9ePfjgg6qscWetW7dumjNnjvLz83X27FmtXr1aJSUlie1du3bVX/7yFxUUFNzUaxPSyZXEmyUA+gS0LGutKiuly5ely0VSUZHzfPmyTSwXFVVvT0X4RorPYa6kOcy7dzeJUE5BQfuclor+AEAc/QEazVqZ8gsyxSfkunJSruLjcl06ItflwzJFx6871URjRDv1VKRgjKI9xirSY6yi3Ubw7fZmRn8AII7+AO2GtXJdPCj34RXyHFwu94V3GlbN7Vek/yyFhyxWeOBcKS2nmRvaerW2/sBaq+IrsenNY49zF2zS+oWLUqRpTkmbnN8fC+90coI78eWcTtXhnk6xYE92drycUXvQerS2PgFA6hDSucr58+e1ZMkSlZc7QzoaY/TlL39ZDz/8sFyu6jnZL1++rM9//vPavHlzouzOO+/Uk08+2aTtqaqq0pIlS3T27NlE2Uc/+lF94QtfSJpiq6ysTF/96lf16quvJsrGjx+vP/7xjzf1+oR0ciXxZgmAPgE3z1qr8vLaAZuiIus8X44HcZznQCC17TXGmXM8Hr7p1lXq1s2oWzdnBJxuXZ3tHk/Hu9BBfwAgjv4ASaJhmfJLTggn9nCVnY8tn5e5ckquK6dkwk03r6Q1Ltm8/orkD1W06zBF84cp2n2kbFa3JnsNNAz9AYA4+gO0V6b4hDwHX3cCO4W7G1THyijabbgifaYp0neqIr0mSr7MZm5p69EW+4No1KqkxLk+dely9fOly9a5ZhVfv9S0U2s1p/R0XXN0nk6dTHLYJ1vqlCNlZ3XMa15oXm2xTwDQPNpaSMfTZEe6hqeffjoR0JGkz372s3rkkUdq7de5c2f98pe/1L333qvDhw9Lkl599VU9+uijGjFiRJO15/e//31SQOd973ufvvzlL9faLysrS9///vdVVFSkDRs2SJJ27NihFStWaOHChU3WHgAA4AiHra5cka7E5ueuuVx8pfa2ostS8OZmqmgyWZlSly5Sfr4zEk6XLlJ+F6MusfVuXZ1tfNMIAABJ0YhMaaFM6VmZiotylcVCOBUXZcpiz+UXZCouy6j5LrJGM/MV7TxI0ZqBnC6DJG96s70mAABAnM3tq9DkRxWa/KhM6Vl5Dq6Q5+Drcp3eds1zICMr9/l9cp/fJ237tazLo2jBaEV6jlekYKyiPUbLZhU43xRCq+ByGWf6qVxp4MCaW2r/HwWDVsXFNa59FceuhRU75YnrZLF9SkqkGhNYtJjKSudx7lxdW699/p6ebpWd7QR24s9Z8eVsU71co7xTtrPs9zs3YAEAaA+adSSdixcvat68eQqFnDtoffv21auvviqv13vNOhs2bNBHPvKRxPrixYv11FNPNUl7QqGQZs+enRjNJjs7WytXrlROzrWHhzx+/LiWLFmSmKpr5MiReu655264DYykkyuJRCsA+oT2zFqrigrn2z+lpVJpWfWyc5HBJoVt4hcZysrrPXSLy8hwQjZJ4Zt8UyOI4zynp3OR4GbQHwCIoz9oR2xUprRQruLjMkXHnWmoYs/mykmZSMslbaNZBYp2GeQ8Og9StMtgRTsPlNJzW6wNaDz6AwBx9AfoaEz5RbkPr5Tn4Otyn9jU6Ok7o5n5ihaMVaRgtKJdhyraZahspx6ScdVfuZWjP0gWiViVltYO7xQXO8GeK1ekklInzFNS4iyXlkpt8Z/N640Fd2Lhnepwj7OckW6Umelcy8tIV2I5MyNWliGlpTmhKbQf9AkA4hhJp4aVK1cmAjqSdP/99183oCNJ06dP18CBA3XkyBFJ0tq1a1VRUaGMjIybbs+mTZuSQjJ33XXXdQM6ktSvXz/NmDFDb731liRp7969OnnypPr06XPT7QEAoDWKRKwqK6XyCqmiQiovd54rKqSystgH+jJbHcKpEcQpi61HUvAtnoZKS5Py8qTOsUde59hznnHKOjvb87tIGRl8cAcA4JqsdUa8KToWC+Eck6vouEzxcbmKT8pEWm6uSev2K5rbWzanj6J5/WNBHCeYI392i7UDAADgZtnMfIXHPKDwmAekymJ5jqyOBXY2NmiaT1f5RbkOr5Tn8MrqY3ozYiMIDnbOkzr1lu3UU9GcXlJaLiPvtFFud/UoPf1qba37/zQScaaPv1JSI7xT4qyXltYO9lwpkUpLUv/lulDIme7+2t+Dr/9mrDHOaD5Xh3fij8xEmMcoLU1K80tp6c6zP7aenla9nJbmjPBD+AcA0FjNGtJZtWpV0vqSJUsaVO+2227T008/LUkKBAJat26dFi1a1OTtue222xpUb8mSJYmQjuSEj2qO9gMAQKpYaxUMSoGAVFklVVVKVQGpqqr6EQ/YOKEb6zyX1yxTUlll/dd7Wp30dCUFbJzQjdS5c3XwJl5G8AYAgEaIRmTKzsmUnJar+GRiRBwniHNCJlTR7E2wLo9sRr5sZlfZrK6ymV0Vzewqm91D0dw+sjl9ZbO6totvhwMAACRJz1V45L0Kj7xXCgflOrtTnpOb5D65Sa6zu2Si4QYdxoQq5D63R+5ze2pts26/bEZn2bRc2fTYIy1PSsuR9WcnHvJnyfpqLPs7SR5/U//EaGZut1GnTlKnTnVtvfY1s3DYqrRMKomFeOLhHSfsYxMj9Vy54nyBLx4Cqqxsth+l0aytvk568fp7NvrYPp9NBHv8abEwj995+LySz1f98NZY9vuMvFdtr7W/V/LXWdfZRkAIANqeZg3pbNu2LbGcn5/f4NFnxo8fn7S+ZcuWJgnpbN26NbHsdrs1ZsyYBtWbMGFCrfYQ0gEAXE847IRngkEpEHuOh2muXg4ErgrWBGxy2Ca2XFklBaqueg6kZu7p5padLeXkSLk5sefc+LpRTk71tnj4Ji2ND6MAADSKjUrBcpmKizLll2QqLsoVezZl52VKzshVclqm9GyDb/40ugme9OTQTWZX2cxYGCcjP7HNpucRwAEAAPD4FO0zRcE+UyR9VgpVyH1qm9xntstVuEfuwj0ygZJGH9ZEAjKlZ6XSs42ua91eWV8stJOWmzifqz63q3l+10VyX3+mBbReHo9RXq6Ul1vX1mtflwsGbWKardJSZ5Tu+KjczrJNHqW7rHq5ovm/D9Bk4td7G/8XePPTsXg8NhHuiQd+vFevX/Uc35YI/XhjYaEa9ZPW663vrLvdXKMFgIZotpDOuXPnVFpamlgfPnx4g+uOGDEiaf3w4cM33Z5oNKpjx44l1vv166fMzMwG1R04cKDS0tJUVVXVZO0BADQPa61CISU9giEpHHu+elvNfUKhq0M0tu5wTc3nQI0QTo3lSOOmC2+3XK7k+aJrBm9yYoGb3KQQjrOfx8MHOgAAaomEpHCVTKhSClU60x2EKmXClU7gJlAmEyyTAqXVy8Eay7HnxHITXBCuj/VmKJrbT9G8frKx52huP9m8frLpnZlaAQAA4EZ5MxQZMFuRAbOddRuVKT4u99k9chXulvv8frkuHbqh4E5DmUhIpvKyVHlZ0onr7mtlZDO6xILY3WSzuiua1S02WmJ32axuimZ1l9JzCWi3Iz6fUX4XZ1r5ul1/9J7y8uTwTknNoE+pVVks7FNRGRstvDy2HBsxPBhqlh+rVQmHncfNZZqa5rOh222dwM41Aj3x0X/qDwSZegNBXq8zUlGXLmH5fEZVlVZer03Uc7slw+dNAK1Us4V0jhw5krTes2fPBtfNz8+X1+tVKOS8ex49evSm23P69GkFAoEbao8xRgUFBYmQz8mTJxUKheT1kvoG0P5YaxWJOCf28efEcqSOstjz1XUS+9WxLRyWolErj6dC4bBUXh5VOOy8bigshYKx8ExsORhy6gSvKq9rn1AH+ODV0tLTpIxMZ07mrEwnRBMP3TgPc9V69faMDIZcBQB0MNZKkaATmgmWy4TKq5eD8eWy2HOFFK50Qjfx8E2t50qZUJWz3kwj2tws60lTNLdvUggnHsqxmV0J4gAAALQE45LNG6Bw3gBpxHudMmtlyi/IdfGgXJcOynXpkFxFx2RKzjijJ9qW+5aZkZWpuChVXJS0/5r7WZfXGX0nFtyx6Xmy/k6yaZ2ktBxFOxdI6blyhV2K+jpJ3nRZT5oz9RYj9bQrHk/1iNp1q/9zRihkE1NclVcoabkyqcwmlgNVV496XqOssmMEf25UJCJVRpwR4G9OQ0NDVtKVOrcY40xDFg8A1RXyqTmlWHxasUQgKBEaqg4MeTySx+0EgNxuyX31utvZx311mbuOMk/d61xLBzqGZh1Jp6aCgoIG1zXGqHv37jp16lSdx2rp9khS9+7dEyGdcDisS5cuNfoYaB2stTp33hndPVFWa5+6Kl5/n+Y4Rp371Hqhpj9GXUX1HqMBbWnIMeo8bgPqxB/xaX9qLkejse1y/t9r7p9UN/asGss2GqtXc7nG8aLR5P2T6lrnpDQarX5EIrZ6OSpFa2yvuR6J14nUWK5jvfbx6z9eXcePh2wiYWe95bSiSYnbEY9HzhzIsUdGhpSRLmXGgjaZGbHQTbpJKsvMlNKv2i89nWFKAQBtgLVSNBQ72QlL0bATaImGnRMcG5aJhCUbO+GJ7evsE4mth2uUVT9MJOSMVhOqiD1XVq+HK52gTSyM44RwKmSi7e+qrfWkK5rT0wniJI2M0182qxvfdgYAAGiNjJHN6qZIVjdF+s9M3hYNy5Sdc8I6VcUylbFHVbFMZZFMVYkzKmNVqUyw1BmJMVAqEwnU/VpN1eRo6LrTb8VjRel1bLPGLXn81aEdT1pSgMe6PJLLI7l9ksvjrLu9sTKPrMsrubzOnXeXV9Yd29/lkYzbCUK5XLFl4zwn1l3O9vg2V3VZzf1svF58m8studzVbXPVbE+NMrcndefc8QvftZ6VvJ5YluRJc362FPN66wv6xDX8+mckYhUIOCOvV9UX6EmMyu6M/h4fqT0Y+9JpfDm+LT5KeyhYe19Gbm8ca5X4f7rJIzVFcxrMGFtnkMflklyxrqXmsttV3eXUtRyvW3O5ruM0ZPn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",
+      "text/plain": [
+       "<Figure size 1400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 301,
+       "width": 1140
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = az.plot_dist(idata.prior[\"mu\"], label=\"Prior\", color=\"C0\", figsize=(14, 3))\n",
+    "az.plot_dist(idata.posterior[\"mu\"], label=\"Posterior\", color=\"C1\", ax=ax)\n",
+    "ax.set(title=r\"Prior and posterior distributions of $\\mu$\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d200233f",
+   "metadata": {},
+   "source": [
+    "## Bayesian hypothesis testing methods\n",
+    "\n",
+    "### Posterior probability statements\n",
+    "\n",
+    "The simplest form of hypothesis testing is to ask whether the mean is greater than zero. This is equivalent to asking whether the probability that $\\mu$ is greater than zero is greater than 0.5. We can compute this directly from the samples. So computing compute $P(\\mu>0 | x)$ is as simple as counting the number of samples where $\\mu>0$ and dividing by the total number of samples - equivalent to computing the mean of the samples where $\\mu>0$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "18369e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9561"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mu_samples = idata.posterior[\"mu\"].values\n",
+    "p_mu_greater_0 = np.mean(mu_samples > 0)\n",
+    "p_mu_greater_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cea999e",
+   "metadata": {},
+   "source": [
+    "We can also include such information in a visual plot using `az.plot_posterior`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d1d2997d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 308,
+       "width": 1105
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(idata, var_names=[\"mu\"], ref_val=0, hdi_prob=\"hide\", figsize=(14, 3));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52913dbd",
+   "metadata": {},
+   "source": [
+    "It could also be that we have some kind of minimum meaningful threshold that we care about. For example, we might care about whether the mean is greater than 0.1. We can compute this in the same way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3790c7e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.944675"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_mu_greater = np.mean(mu_samples > 0.1)\n",
+    "p_mu_greater"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6511a148",
+   "metadata": {},
+   "source": [
+    "### Highest Density Intervals (HDIs)\n",
+    "\n",
+    "The HDI gives an interval of highest probability density. If zero is outside the HDI, it’s unlikely the parameter is near zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8aed0b24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.13571603,  2.79383736])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hdi_mu = az.hdi(idata.posterior[\"mu\"])[\"mu\"].data\n",
+    "hdi_mu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c05274a2",
+   "metadata": {},
+   "source": [
+    "In this case, zero is within the HDI, so based on this measure, we can't express much confidence that the mean is different from zero."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3365bc1",
+   "metadata": {},
+   "source": [
+    "Again, we can use `az.plot_posterior` to visualize the HDIs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4c67ca02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 308,
+       "width": 1105
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(idata, var_names=[\"mu\"], figsize=(14, 3));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bc616ec",
+   "metadata": {},
+   "source": [
+    "### Region of Practical Equivalence (ROPE)\n",
+    "\n",
+    "If the probability that the parameter is within a certain range is high, we can say that the parameter is practically equivalent to that value. This is a useful way to express that we don't care about small differences. \n",
+    "\n",
+    "For example, if we state that values within $-0.1$ to $0.1$ (this region need not be symmetric) are practically equivalent to zero, we can compute the probability that $\\mu$ is within this range. If this probability is high enough then we can say that the mean is practically equivalent to zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "48b6496f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.021975"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rope = [-0.1, 0.1]\n",
+    "p_in_rope = np.mean((mu_samples > rope[0]) & (mu_samples < rope[1]))\n",
+    "p_in_rope"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d2cd3c3",
+   "metadata": {},
+   "source": [
+    "So there is only a 2.2% probability that the mean is practically equivalent to zero. This is sufficiently low that we can reject the hypothesis that the mean is practically equivalent to zero."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a623e4a",
+   "metadata": {},
+   "source": [
+    "Third time in a row, `arviz` has our back and can plot the ROPE and HDIs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1cdea94a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 308,
+       "width": 1105
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(idata, var_names=[\"mu\"], rope=rope, figsize=(14, 3));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc84f7a",
+   "metadata": {},
+   "source": [
+    "The intuition we can get from this is that if the ROPE is narrow, we would require quite a high level of precision to accept the null hypthesis. The posterior distribution would have to be very tightly centered around the null value to have a large probability of being within the ROPE."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46cf08f2",
+   "metadata": {},
+   "source": [
+    "### HDI+ROPE decision criteria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de465219",
+   "metadata": {},
+   "source": [
+    "{cite:t}`kruschke2018rejecting` outlines the HDI+ROPE decision rule, which is summarised in the figure taken from that paper. Namely:\n",
+    "\n",
+    "* **Accept the null value**: If the HDI falls entirely within the ROPE. The HDI does not need to include the null value.\n",
+    "* **Reject the null value**: If the HDI falls entirely outside the ROPE. \n",
+    "* **Remain undecided**: If the HDI is partially or fully outside the ROPE.\n",
+    "\n",
+    "In our case, looking back at our posterior + ROPE plot above, we would remain undecided because the HDI does not fall entirely outside nor within the ROPE.\n",
+    "\n",
+    "![](hdi_plus_rope_decision_rule.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0524af06",
+   "metadata": {},
+   "source": [
+    "### Bayes Factors\n",
+    "\n",
+    "[Bayes Factors](https://en.wikipedia.org/wiki/Bayes_factor) provide a Bayesian alternative to classical hypothesis tests, allowing you to weigh evidence for one hypothesis relative to another. In the simplest case—testing whether $\\mu=0$ versus $\\mu \\neq 0$ — the Bayes Factor (BF) tells you how much more (or less) likely your observed data are under the model where $\\mu=0$ than under the model where $\\mu$ is free to vary.\n",
+    "\n",
+    "Intuitively, the Bayes Factor can be understood by comparing the density of $\\mu$ at zero before and after observing the data. Before collecting data, you have a prior belief about $\\mu$. This prior density at $\\mu=0$ represents how plausible zero was considered initially. After seeing the data, you update these beliefs to get the posterior distribution. The posterior density at $\\mu=0$ indicates how plausible zero remains given the evidence. The ratio of these densities—the Savage-Dickey ratio—is closely related to the Bayes Factor. If the data make \n",
+    "$\\mu=0$ more plausible relative to your initial belief, the Bayes Factor will favor  $\\mu=0$. If the data diminish the credibility of $\\mu=0$, the Bayes Factor will favor $\\mu\\neq0$\n",
+    "\n",
+    "Because the Bayes Factor directly compares how the data update the prior odds of each hypothesis, the choice of prior is crucial. A strong prior concentration at $\\mu=0$ could make it harder for data to move the posterior density away from zero, influencing the resulting Bayes Factor. On the other hand, a diffuse prior might make it easier for data to shift your beliefs about $\\mu$. Thus, specifying a reasonable and justifiable prior distribution is essential when using Bayes Factors for hypothesis testing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "645e7fb3",
+   "metadata": {},
+   "source": [
+    "Yet again, `arviz` has a function to help us here. We can use `plot_bf` to compute the Bayes Factor for the hypothesis that $\\mu=0$ versus $\\mu\\neq0$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "16003a42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 351,
+       "width": 1166
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_bf(idata, var_name=\"mu\", ref_val=0, figsize=(14, 3));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10d77d2",
+   "metadata": {},
+   "source": [
+    "We can see that the probability of $\\mu=0$ has gone _down_ after observing the data. This is reflected in the value of $BF_{01}=0.54$ in that it is less than 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4283bdd3",
+   "metadata": {},
+   "source": [
+    "Readers are referred to {ref}`Bayes_factor` for a more detailed look at Bayes Factors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "650f409c",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "**Posterior Probability Statements**  \n",
+    "- *Idea:* Compute $P(\\theta > \\delta \\mid \\text{data})$ directly from the posterior.  \n",
+    "- *Pros:* Simple, intuitive, no special tools needed.  \n",
+    "- *Cons:* Requires choosing a threshold $\\delta$.\n",
+    "\n",
+    "**Highest Density Intervals (HDIs)**  \n",
+    "- *Idea:* Identify the range of values containing a fixed portion (e.g., 95%) of the posterior mass.  \n",
+    "- *Pros:* Provides a clear summary of where the parameter lies; easy to interpret.  \n",
+    "- *Cons:* By itself, doesn’t encode a decision rule; must still choose what HDI exclusion implies.\n",
+    "\n",
+    "**ROPE (Region of Practical Equivalence)**  \n",
+    "- *Idea:* Define a small interval around the null (e.g., zero) representing negligible effect size and assess posterior mass within it.  \n",
+    "- *Pros:* Focuses on practical rather than just statistical significance; flexible.  \n",
+    "- *Cons:* Requires subjective definition of what counts as negligible.\n",
+    "\n",
+    "**ROPE + HDI Decision Rule**  \n",
+    "- *Idea:* Combine ROPE with HDI to classify results as negligible, meaningful, or inconclusive.  \n",
+    "- *Pros:* Offers a three-way decision with practical interpretation; balances interval uncertainty and practical thresholds.  \n",
+    "- *Cons:* Still needs careful definition of ROPE bounds and HDI level.\n",
+    "\n",
+    "**Bayes Factors**  \n",
+    "- *Idea:* Compare evidence for one model/hypothesis against another by ratio of marginal likelihoods.  \n",
+    "- *Pros:* Provides a direct measure of relative evidence; can be viewed as updating prior odds.  \n",
+    "- *Cons:* Sensitive to priors; can be computationally challenging; interpreting BF scales can be tricky."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "763e2a54",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "* Authored by [Benjamin T. Vincent](https://github.com/drbenvincent) in December, 2024."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b3bbd1b",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aec5c13",
+   "metadata": {},
+   "source": [
+    "## Watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1f25d6b6-f0a1-4ca3-96f5-9a5a858bb461",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Mon Dec 09 2024\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.12.8\n",
+      "IPython version      : 8.30.0\n",
+      "\n",
+      "pytensor: 2.26.4\n",
+      "xarray  : 2024.11.0\n",
+      "\n",
+      "matplotlib: 3.9.3\n",
+      "numpy     : 1.26.4\n",
+      "arviz     : 0.20.0\n",
+      "pymc      : 5.19.1\n",
+      "\n",
+      "Watermark: 2.5.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w -p pytensor,xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818c2750-89d5-4310-8df2-91b1e531afe7",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymc_env",
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/howto/hypothesis_testing.myst.md b/examples/howto/hypothesis_testing.myst.md
new file mode 100644
index 00000000..9ebcca19
--- /dev/null
+++ b/examples/howto/hypothesis_testing.myst.md
@@ -0,0 +1,288 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: pymc_env
+  language: python
+  name: python3
+---
+
+(hypothesis-testing)=
+# Bayesian Hypothesis Testing - an introduction
+
+:::{post} December 2024
+:tags: hypothesis testing, bayesian decision theory
+:category: beginner 
+:author: Benjamin T. Vincent
+:::
+
+```{code-cell} ipython3
+import arviz as az
+import matplotlib.pyplot as plt
+import numpy as np
+import pymc as pm
+```
+
+```{code-cell} ipython3
+%config InlineBackend.figure_format = 'retina'
+az.style.use("arviz-darkgrid")
+plt.rcParams.update({"font.size": 14, "figure.constrained_layout.use": False})
+SEED = 42
+rng = np.random.default_rng(SEED)
+```
+
+## Introduction
+
+Bayesian hypothesis testing provides a flexible and intuitive way to assess whether parameters differ from specified values. Unlike classical methods focusing on p-values, Bayesian methods let us directly compute probabilities of hypotheses and quantify the strength of evidence in various ways.
+
+In this tutorial, we'll use PyMC to:
+
+* Fit a simple model to synthetic data and obtain samples from the prior and posterior distributions.
+* Demonstrate the following Bayesian hypothesis testing methods:
+  * Posterior probability statements
+  * Highest Density Intervals (HDIs)
+  * Regions of Practical Equivalence (ROPE)
+  * Bayes Factors
+
+We'll work through a scenario where we want to know if the mean of some metric (e.g., monthly profit) is different from zero.
+
++++
+
+:::{note}
+Many Bayesian practitioners argue that collapsing a rich posterior distribution into a single binary decision (e.g., "reject" or "fail to reject") undermines the essence of Bayesian inference. The Bayesian perspective values the entire posterior as a nuanced representation of uncertainty about parameters. Reducing it to yes/no decisions discards that nuance and may mislead by oversimplifying the uncertainty involved.
+
+However, in real-world scenarios—such as policy-making, resource allocation, or medical decision-making—practitioners often must choose a single course of action. In such cases, translating the posterior into a decision rule or threshold is necessary. The key is to do so transparently and thoughtfully, ideally incorporating utilities, costs, and the full breadth of uncertainty in the decision process.
+:::
+
++++
+
+### Parameter estimation vs model comparison
+
+The Bayesian evaluation of null values can proceed in two distinct ways (see {cite:t}`kruschke2011bayesian`):
+
+#### Parameter estimation
+The parameter estimation approach considers a model where the parameter is allowed to vary (which includes the null value). We then compute the posterior distribution of this value and come up with some kind of decision rule which determines if we accept or reject the null value.
+
+#### Model comparison
+Two competing models are considered. The first model assumes that the null value is true, or fixed, and the second model allows a range of values. The models are compared to see which is more supported by the data. An example would be in assessing if a coin is fair (null hypothesis) or biased (alternative hypothesis) - we would set up a model where the coin has a fixed probability of heads (0.5) and another model where the probability of heads is a free parameter. Readers are referred to the PyMC examples focussing on {ref}`pymc:model_comparison` for more details.
+
++++
+
+## Setting up the example
+
+Rather than focus on a complex example, we'll pick a trivial one were we are simply estimating the mean of a single variable. This will allow us to focus on the hypothesis testing. The important thing is what we do with our MCMC samples, not the particulars of the model.
+
+```{code-cell} ipython3
+true_mu = 2.0
+true_sigma = 3.0
+n = 12
+
+x = rng.normal(loc=true_mu, scale=true_sigma, size=n)
+
+fig, ax = plt.subplots(figsize=(14, 1))
+ax.plot(x, np.zeros_like(x), "ro")
+ax.set(yticklabels=[], yticks=[], xlabel="x", title="Observations");
+```
+
+## Sampling from the prior and posterior
+
+Now we'll build our simple model. Again, the focus here is not on the model of the data as such, but simply obtaining a meaningful prior and posterior distribution. We'll ask for more MCMC samples than we normally do, so that we can get a more accurate approximation of the prior and posterior distributions.
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+with pm.Model() as model:
+    # priors
+    mu = pm.Normal("mu", mu=0, sigma=2)
+    sigma = pm.Gamma("sigma", alpha=2, beta=1)
+    # likelihood
+    pm.Normal("y", mu=mu, sigma=sigma, observed=x)
+    # sample
+    idata = pm.sample_prior_predictive(samples=10_000)
+    idata.extend(pm.sample(draws=10_000))
+```
+
+We didn't get any warnings from the sampling processes.
+
+It is good practice to visualise the posterior distribution, so below we'll look at the joint posterior over $\mu$ and $\sigma$ parameters. Everything looks fine here.
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+az.plot_pair(idata, var_names=["mu", "sigma"], marginals=True, point_estimate="mean");
+```
+
+Finally, seeing as $\mu$ is the core parameter of interest, we'll visualise both the prior and posterior distributions for $\mu$.
+
+```{code-cell} ipython3
+:tags: [hide-input]
+
+ax = az.plot_dist(idata.prior["mu"], label="Prior", color="C0", figsize=(14, 3))
+az.plot_dist(idata.posterior["mu"], label="Posterior", color="C1", ax=ax)
+ax.set(title=r"Prior and posterior distributions of $\mu$");
+```
+
+## Bayesian hypothesis testing methods
+
+### Posterior probability statements
+
+The simplest form of hypothesis testing is to ask whether the mean is greater than zero. This is equivalent to asking whether the probability that $\mu$ is greater than zero is greater than 0.5. We can compute this directly from the samples. So computing compute $P(\mu>0 | x)$ is as simple as counting the number of samples where $\mu>0$ and dividing by the total number of samples - equivalent to computing the mean of the samples where $\mu>0$.
+
+```{code-cell} ipython3
+mu_samples = idata.posterior["mu"].values
+p_mu_greater_0 = np.mean(mu_samples > 0)
+p_mu_greater_0
+```
+
+We can also include such information in a visual plot using `az.plot_posterior`.
+
+```{code-cell} ipython3
+az.plot_posterior(idata, var_names=["mu"], ref_val=0, hdi_prob="hide", figsize=(14, 3));
+```
+
+It could also be that we have some kind of minimum meaningful threshold that we care about. For example, we might care about whether the mean is greater than 0.1. We can compute this in the same way.
+
+```{code-cell} ipython3
+p_mu_greater = np.mean(mu_samples > 0.1)
+p_mu_greater
+```
+
+### Highest Density Intervals (HDIs)
+
+The HDI gives an interval of highest probability density. If zero is outside the HDI, it’s unlikely the parameter is near zero.
+
+```{code-cell} ipython3
+hdi_mu = az.hdi(idata.posterior["mu"])["mu"].data
+hdi_mu
+```
+
+In this case, zero is within the HDI, so based on this measure, we can't express much confidence that the mean is different from zero.
+
++++
+
+Again, we can use `az.plot_posterior` to visualize the HDIs.
+
+```{code-cell} ipython3
+az.plot_posterior(idata, var_names=["mu"], figsize=(14, 3));
+```
+
+### Region of Practical Equivalence (ROPE)
+
+If the probability that the parameter is within a certain range is high, we can say that the parameter is practically equivalent to that value. This is a useful way to express that we don't care about small differences. 
+
+For example, if we state that values within $-0.1$ to $0.1$ (this region need not be symmetric) are practically equivalent to zero, we can compute the probability that $\mu$ is within this range. If this probability is high enough then we can say that the mean is practically equivalent to zero.
+
+```{code-cell} ipython3
+rope = [-0.1, 0.1]
+p_in_rope = np.mean((mu_samples > rope[0]) & (mu_samples < rope[1]))
+p_in_rope
+```
+
+So there is only a 2.2% probability that the mean is practically equivalent to zero. This is sufficiently low that we can reject the hypothesis that the mean is practically equivalent to zero.
+
++++
+
+Third time in a row, `arviz` has our back and can plot the ROPE and HDIs.
+
+```{code-cell} ipython3
+az.plot_posterior(idata, var_names=["mu"], rope=rope, figsize=(14, 3));
+```
+
+The intuition we can get from this is that if the ROPE is narrow, we would require quite a high level of precision to accept the null hypthesis. The posterior distribution would have to be very tightly centered around the null value to have a large probability of being within the ROPE.
+
++++
+
+### HDI+ROPE decision criteria
+
++++
+
+{cite:t}`kruschke2018rejecting` outlines the HDI+ROPE decision rule, which is summarised in the figure taken from that paper. Namely:
+
+* **Accept the null value**: If the HDI falls entirely within the ROPE. The HDI does not need to include the null value.
+* **Reject the null value**: If the HDI falls entirely outside the ROPE. 
+* **Remain undecided**: If the HDI is partially or fully outside the ROPE.
+
+In our case, looking back at our posterior + ROPE plot above, we would remain undecided because the HDI does not fall entirely outside nor within the ROPE.
+
+![](hdi_plus_rope_decision_rule.png)
+
++++
+
+### Bayes Factors
+
+[Bayes Factors](https://en.wikipedia.org/wiki/Bayes_factor) provide a Bayesian alternative to classical hypothesis tests, allowing you to weigh evidence for one hypothesis relative to another. In the simplest case—testing whether $\mu=0$ versus $\mu \neq 0$ — the Bayes Factor (BF) tells you how much more (or less) likely your observed data are under the model where $\mu=0$ than under the model where $\mu$ is free to vary.
+
+Intuitively, the Bayes Factor can be understood by comparing the density of $\mu$ at zero before and after observing the data. Before collecting data, you have a prior belief about $\mu$. This prior density at $\mu=0$ represents how plausible zero was considered initially. After seeing the data, you update these beliefs to get the posterior distribution. The posterior density at $\mu=0$ indicates how plausible zero remains given the evidence. The ratio of these densities—the Savage-Dickey ratio—is closely related to the Bayes Factor. If the data make 
+$\mu=0$ more plausible relative to your initial belief, the Bayes Factor will favor  $\mu=0$. If the data diminish the credibility of $\mu=0$, the Bayes Factor will favor $\mu\neq0$
+
+Because the Bayes Factor directly compares how the data update the prior odds of each hypothesis, the choice of prior is crucial. A strong prior concentration at $\mu=0$ could make it harder for data to move the posterior density away from zero, influencing the resulting Bayes Factor. On the other hand, a diffuse prior might make it easier for data to shift your beliefs about $\mu$. Thus, specifying a reasonable and justifiable prior distribution is essential when using Bayes Factors for hypothesis testing.
+
++++
+
+Yet again, `arviz` has a function to help us here. We can use `plot_bf` to compute the Bayes Factor for the hypothesis that $\mu=0$ versus $\mu\neq0$.
+
+```{code-cell} ipython3
+az.plot_bf(idata, var_name="mu", ref_val=0, figsize=(14, 3));
+```
+
+We can see that the probability of $\mu=0$ has gone _down_ after observing the data. This is reflected in the value of $BF_{01}=0.54$ in that it is less than 1.
+
++++
+
+Readers are referred to {ref}`Bayes_factor` for a more detailed look at Bayes Factors.
+
++++
+
+## Summary
+
+**Posterior Probability Statements**  
+- *Idea:* Compute $P(\theta > \delta \mid \text{data})$ directly from the posterior.  
+- *Pros:* Simple, intuitive, no special tools needed.  
+- *Cons:* Requires choosing a threshold $\delta$.
+
+**Highest Density Intervals (HDIs)**  
+- *Idea:* Identify the range of values containing a fixed portion (e.g., 95%) of the posterior mass.  
+- *Pros:* Provides a clear summary of where the parameter lies; easy to interpret.  
+- *Cons:* By itself, doesn’t encode a decision rule; must still choose what HDI exclusion implies.
+
+**ROPE (Region of Practical Equivalence)**  
+- *Idea:* Define a small interval around the null (e.g., zero) representing negligible effect size and assess posterior mass within it.  
+- *Pros:* Focuses on practical rather than just statistical significance; flexible.  
+- *Cons:* Requires subjective definition of what counts as negligible.
+
+**ROPE + HDI Decision Rule**  
+- *Idea:* Combine ROPE with HDI to classify results as negligible, meaningful, or inconclusive.  
+- *Pros:* Offers a three-way decision with practical interpretation; balances interval uncertainty and practical thresholds.  
+- *Cons:* Still needs careful definition of ROPE bounds and HDI level.
+
+**Bayes Factors**  
+- *Idea:* Compare evidence for one model/hypothesis against another by ratio of marginal likelihoods.  
+- *Pros:* Provides a direct measure of relative evidence; can be viewed as updating prior odds.  
+- *Cons:* Sensitive to priors; can be computationally challenging; interpreting BF scales can be tricky.
+
++++
+
+## Authors
+* Authored by [Benjamin T. Vincent](https://github.com/drbenvincent) in December, 2024.
+
++++
+
+## References
+:::{bibliography}
+:filter: docname in docnames
+:::
+
++++
+
+## Watermark
+
+```{code-cell} ipython3
+%load_ext watermark
+%watermark -n -u -v -iv -w -p pytensor,xarray
+```
+
+:::{include} ../page_footer.md
+:::
diff --git a/examples/references.bib b/examples/references.bib
index 8014cbaf..c10f39c2 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -937,3 +937,23 @@ @article{Yao_2022
 doi = {10.1214/21-BA1287},
 URL = {https://doi.org/10.1214/21-BA1287}
 }
+@article{kruschke2010believe,
+  title={What to believe: Bayesian methods for data analysis},
+  author={Kruschke, John K},
+  journal={Trends in cognitive sciences},
+  volume={14},
+  number={7},
+  pages={293--300},
+  year={2010},
+  publisher={Elsevier}
+}
+@article{kruschke2018rejecting,
+  title={Rejecting or accepting parameter values in Bayesian estimation},
+  author={Kruschke, John K},
+  journal={Advances in methods and practices in psychological science},
+  volume={1},
+  number={2},
+  pages={270--280},
+  year={2018},
+  publisher={Sage Publications Sage CA: Los Angeles, CA}
+}
\ No newline at end of file