examples/eight schools example.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Simple example\n",
    "\n",
    "To illustrate the basic functionallity, we'll fit the eight schools model (the \"hello world\" of bayesian models).\n",
    "\n",
    "We begin by importing the Stan2tfp object, and plotting functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stan2tfp import Stan2tfp\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import arviz as az"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model itself is written in Stan, and is specified as a multiline string: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stan_code = \"\"\"\n",
    "data {\n",
    "  int<lower=0> J;\n",
    "  real y[J];\n",
    "  real<lower=0> sigma[J];\n",
    "}\n",
    "\n",
    "parameters {\n",
    "  real mu;\n",
    "  real<lower=0> tau;\n",
    "  vector[J] theta_tilde;\n",
    "}\n",
    "\n",
    "transformed parameters {\n",
    "  vector[J] theta = mu + tau * theta_tilde;\n",
    "}\n",
    "\n",
    "model {\n",
    "  mu ~ normal(0, 5);\n",
    "  tau ~ normal(0, 5);\n",
    "  theta_tilde ~ normal(0, 1);\n",
    "  y ~ normal(theta, sigma);\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is specified using a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "eight_schools_data_dict = dict(\n",
    "    J=8,\n",
    "    y=[28, 8, -3, 7, -1, 1, 18, 12],\n",
    "    sigma=[15, 10, 16, 11, 9, 11, 10, 18]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the model object itself is created: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading the latest stan2tfp compiler...\n",
      "Download complete, saved to: /Users/adamhaber/projects/stan2tfp/stan2tfp/stan2tfp_compiler.exe\n",
      "Compiling stan file to tfp file...\n"
     ]
    }
   ],
   "source": [
    "model = Stan2tfp(stan_model_code=stan_code, data_dict=eight_schools_data_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also instantiate the model using an external `.stan` file. Passing the data dict at this point is not necessary, as it's not needed for the tranlation of Stan code; we could've used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling stan file to tfp file...\n"
     ]
    }
   ],
   "source": [
    "model = Stan2tfp(stan_model_code=stan_code);\n",
    "# # do whatever you want here\n",
    "model.init_model(data_dict=eight_schools_data_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that since this is the first time we're calling the compiler, `stan2tfp` automatically downloads the latest pre-compiled binary, and informs you about its path.\n",
    "\n",
    "To inspect the emitted TFP code, call `get_tfp_code()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "import numpy as np__\n",
      "import tensorflow as tf__\n",
      "import tensorflow_probability as tfp__\n",
      "tfd__ = tfp__.distributions\n",
      "tfb__ = tfp__.bijectors\n",
      "from tensorflow.python.ops.parallel_for import pfor as pfor__\n",
      "\n",
      "class nameuwktdqn_model(tfd__.Distribution):\n",
      "\n",
      "  def __init__(self, J, y, sigma):\n",
      "    self.J = J\n",
      "    self.y = tf__.cast(y, tf__.float64)\n",
      "    self.sigma = tf__.cast(sigma, tf__.float64)\n",
      "    \n",
      "     \n",
      "  \n",
      "  def log_prob_one_chain(self, params):\n",
      "    target = 0\n",
      "    \n",
      "    # Data\n",
      "    J = self.J\n",
      "    y = self.y\n",
      "    sigma = self.sigma\n",
      "    \n",
      "    # Transformed data\n",
      "    \n",
      "    \n",
      "    # Parameters\n",
      "    mu = tf__.cast(params[0], tf__.float64)\n",
      "    tau = tf__.cast(params[1], tf__.float64)\n",
      "    theta_tilde = tf__.cast(params[2], tf__.float64)\n",
      "    \n",
      "    # Target log probability computation\n",
      "    theta = mu + (tau * theta_tilde)\n",
      "    target += tf__.reduce_sum(tfd__.Normal(tf__.cast(0, tf__.float64),\n",
      "                                           tf__.cast(5, tf__.float64)).log_prob(mu))\n",
      "    target += tf__.reduce_sum(tfd__.Normal(tf__.cast(0, tf__.float64),\n",
      "                                           tf__.cast(5, tf__.float64)).log_prob(tau))\n",
      "    target += tf__.reduce_sum(tfd__.Normal(tf__.cast(0, tf__.float64),\n",
      "                                           tf__.cast(1, tf__.float64)).log_prob(theta_tilde))\n",
      "    target += tf__.reduce_sum(tfd__.Normal(theta, sigma).log_prob(y))\n",
      "    return target\n",
      "     \n",
      "  def log_prob(self, params):\n",
      "    return tf__.vectorized_map(self.log_prob_one_chain, params)\n",
      "    \n",
      "     \n",
      "  def parameter_shapes(self, nchains__):\n",
      "    J = self.J\n",
      "    y = self.y\n",
      "    sigma = self.sigma\n",
      "    return [(nchains__, ), (nchains__, ), (nchains__, J)]\n",
      "     \n",
      "  def parameter_bijectors(self):\n",
      "    J = self.J\n",
      "    y = self.y\n",
      "    sigma = self.sigma\n",
      "    return [tfb__.Identity(),\n",
      "            tfb__.Chain([tfb__.Shift(tf__.cast(0, tf__.float64)), tfb__.Exp()]),\n",
      "            tfb__.Identity()]\n",
      "     \n",
      "  def parameter_names(self):\n",
      "    return [\"mu\", \"tau\", \"theta_tilde\"]\n",
      "     \n",
      "model = nameuwktdqn_model\n"
     ]
    }
   ],
   "source": [
    "print(model.get_tfp_code())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Internally, this code is now `eval`ed by the interpreter and creates the necessary _python_ objects in the current namespace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fit the model, we call the `sample` method; it's a wrapper around TFP's MCMC machinery, aiming for sensible defaults and ease-of-use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcmc_trace, kernel_results = model.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`model.sample` returns the actual samples (`mcmc_trace`) and TFP's `kernel_results` (holding important sampler diagnostics). Since we're in TFP-world now, we can use any tool from the TFP ecosystem we like; specifically, we can use the excellent [Arviz](https://arviz-devs.github.io) library for plotting: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adamhaber/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3326: UserWarning: tfp.edward2 module is deprecated and will be removed on 2019-12-01. Use https://github.com/google/edward2 library instead.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0517 15:31:11.815162 4598851008 deprecation.py:323] From /Users/adamhaber/miniconda3/envs/stan2tfp-dev/lib/python3.6/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "data_for_az = az.from_tfp(posterior=mcmc_trace, var_names=['mu','trace','theta_tilde'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-98dd0cc6-dba1-423c-b4c1-508c290e9e97' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-98dd0cc6-dba1-423c-b4c1-508c290e9e97' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 4</li><li><span class='xr-has-index'>theta_tilde_dim_0</span>: 4</li><li><span class='xr-has-index'>theta_tilde_dim_1</span>: 8</li><li><span class='xr-has-index'>trace_dim_0</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e857714c-8fce-4974-9db5-51e30b8cedd5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e857714c-8fce-4974-9db5-51e30b8cedd5' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-90ee758f-a73e-4db9-87ac-2217dfc28736' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90ee758f-a73e-4db9-87ac-2217dfc28736' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a23fec0-a640-4096-9c54-ac9c18da3795' class='xr-var-data-in' type='checkbox'><label for='data-2a23fec0-a640-4096-9c54-ac9c18da3795' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([0])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-9ba2d4eb-6f21-4d4c-b4c4-084f8e3dcbdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ba2d4eb-6f21-4d4c-b4c4-084f8e3dcbdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9007f3f2-e0f2-48c5-adf8-9de383a8d6a8' class='xr-var-data-in' type='checkbox'><label for='data-9007f3f2-e0f2-48c5-adf8-9de383a8d6a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([  0,   1,   2, ..., 997, 998, 999])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-7237bdba-6efc-46b0-8177-9f00b50f40f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7237bdba-6efc-46b0-8177-9f00b50f40f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e08ce550-7fca-480e-8321-b9925311e53e' class='xr-var-data-in' type='checkbox'><label for='data-e08ce550-7fca-480e-8321-b9925311e53e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([0, 1, 2, 3])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trace_dim_0</span></div><div class='xr-var-dims'>(trace_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-aa463207-fc96-4ed0-9445-f2b8c164b819' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa463207-fc96-4ed0-9445-f2b8c164b819' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a73caa8f-a3ed-46b9-bd40-0c03b255d022' class='xr-var-data-in' type='checkbox'><label for='data-a73caa8f-a3ed-46b9-bd40-0c03b255d022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([0, 1, 2, 3])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>theta_tilde_dim_0</span></div><div class='xr-var-dims'>(theta_tilde_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e2f6356b-0c51-4d0b-9cff-d7bf5136f925' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2f6356b-0c51-4d0b-9cff-d7bf5136f925' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb50c35e-6c40-44a1-a24a-3538c66ebabf' class='xr-var-data-in' type='checkbox'><label for='data-fb50c35e-6c40-44a1-a24a-3538c66ebabf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([0, 1, 2, 3])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>theta_tilde_dim_1</span></div><div class='xr-var-dims'>(theta_tilde_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-e1972c71-a2ff-4369-9206-ad5dc1b69293' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1972c71-a2ff-4369-9206-ad5dc1b69293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f287ce97-ad86-4b95-b33c-7acb2d445fd6' class='xr-var-data-in' type='checkbox'><label for='data-f287ce97-ad86-4b95-b33c-7acb2d445fd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-5e9227e8-482d-4c56-b7ce-42d4c0961cd0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e9227e8-482d-4c56-b7ce-42d4c0961cd0' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.696 2.455 4.138 ... 7.722 3.266</div><input id='attrs-2c76ec23-7db9-4ae3-812e-1d20d276c217' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c76ec23-7db9-4ae3-812e-1d20d276c217' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2882b63-20ed-45f3-ba15-992fbc1c68f0' class='xr-var-data-in' type='checkbox'><label for='data-f2882b63-20ed-45f3-ba15-992fbc1c68f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[5.69555092, 2.45493752, 4.13815295, 3.34585148],\n",
       "        [4.76197334, 1.38947978, 4.12607903, 5.19875302],\n",
       "        [3.75592496, 1.38947978, 5.72440998, 7.47097197],\n",
       "        ...,\n",
       "        [4.81703287, 3.83061212, 5.27919206, 3.51931821],\n",
       "        [4.76221165, 3.96592547, 4.58793363, 5.99396352],\n",
       "        [4.90415542, 2.79666044, 7.72193481, 3.26559271]]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>trace</span></div><div class='xr-var-dims'>(chain, draw, trace_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.985 5.002 1.463 ... 4.693 5.168</div><input id='attrs-36739b9d-ea16-442f-9d1a-d2e9a63aade3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36739b9d-ea16-442f-9d1a-d2e9a63aade3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-264afad6-d473-4d9b-b064-c753711a1799' class='xr-var-data-in' type='checkbox'><label for='data-264afad6-d473-4d9b-b064-c753711a1799' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[2.98494426, 5.00238081, 1.46275495, 0.54864736],\n",
       "        [6.68634971, 7.70314159, 0.54347319, 0.80290352],\n",
       "        [0.92605018, 7.70314159, 0.662535  , 8.61772285],\n",
       "        ...,\n",
       "        [3.02464615, 2.3120839 , 4.28966195, 2.01740518],\n",
       "        [1.32498204, 3.17669117, 5.49097196, 0.63747984],\n",
       "        [0.85090382, 2.51622044, 4.69329374, 5.16785986]]])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>theta_tilde</span></div><div class='xr-var-dims'>(chain, draw, theta_tilde_dim_0, theta_tilde_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4164 0.3542 ... -0.4372 0.8145</div><input id='attrs-28acbd13-8074-457f-8c60-a2e7d4b5e3ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28acbd13-8074-457f-8c60-a2e7d4b5e3ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-645e1e34-ef43-4390-88a0-b616cf1f475c' class='xr-var-data-in' type='checkbox'><label for='data-645e1e34-ef43-4390-88a0-b616cf1f475c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([[[[-0.41644483,  0.35423957, -1.08242959, ..., -1.08594373,\n",
       "           2.02895259,  1.51993335],\n",
       "         [ 1.51620584, -0.05035345, -0.83174702, ..., -1.85730002,\n",
       "          -0.44248049,  0.85145507],\n",
       "         [-1.00361288, -1.60836038,  0.46955581, ..., -0.63428296,\n",
       "           0.22961875,  0.27855506],\n",
       "         [-0.33062923,  0.04779014, -0.41055864, ...,  0.01016464,\n",
       "           0.50331941,  0.95075349]],\n",
       "\n",
       "        [[ 0.71409202, -0.11448924, -0.67923159, ...,  0.22451119,\n",
       "          -1.23311145, -0.58761198],\n",
       "         [-0.17499853,  0.47332943,  0.44899998, ...,  1.57073908,\n",
       "           1.70384258, -0.96754113],\n",
       "         [-0.80314967, -1.54595811,  0.73949076, ..., -0.69718274,\n",
       "          -0.36685058,  1.65458504],\n",
       "         [ 0.47521413, -0.30448042,  1.43326048, ..., -0.93924744,\n",
       "          -0.77076377, -0.83380055]],\n",
       "\n",
       "        [[ 1.97106927,  0.06986902, -0.51262758, ..., -2.09529761,\n",
       "          -0.91075486,  0.3799377 ],\n",
       "         [-0.17499853,  0.47332943,  0.44899998, ...,  1.57073908,\n",
       "           1.70384258, -0.96754113],\n",
       "         [ 1.18212965,  0.97624779,  0.42291783, ...,  0.70558411,\n",
       "           0.89267666, -2.20151561],\n",
       "         [ 0.23755216, -0.48098019, -0.69381569, ..., -0.08952226,\n",
       "           0.3469958 ,  0.68461943]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 0.62150554, -0.0299674 , -0.48125367, ...,  0.6847554 ,\n",
       "          -1.59258074,  0.79669391],\n",
       "         [ 1.03603019,  0.87526363,  0.41526278, ...,  1.44775688,\n",
       "          -0.95275032, -0.73525416],\n",
       "         [ 0.66109301,  1.57118918,  1.07850283, ...,  0.96511163,\n",
       "           0.89465356,  0.19348217],\n",
       "         [ 1.70233981,  0.09251318, -1.78433241, ..., -0.09039071,\n",
       "          -0.62869484, -0.90135118]],\n",
       "\n",
       "        [[ 0.68663471, -0.63778393, -1.11797548, ...,  0.26099028,\n",
       "          -1.25272721,  1.03424356],\n",
       "         [-0.30914337, -0.57303345, -0.55750277, ..., -1.84391131,\n",
       "           1.65590757,  0.85542958],\n",
       "         [ 1.03604505, -1.25627742, -0.81543019, ..., -0.77374802,\n",
       "           0.35787605, -0.58033256],\n",
       "         [-0.46985083, -0.50573545,  0.58168023, ...,  2.30305503,\n",
       "           0.12116949, -1.14328313]],\n",
       "\n",
       "        [[ 0.79544429, -0.72479209, -2.08235911, ...,  0.56573517,\n",
       "          -0.49912405,  1.47450869],\n",
       "         [ 1.75936283, -2.12331572, -0.25300208, ..., -1.21134913,\n",
       "           1.86506251, -0.66231337],\n",
       "         [ 0.44522199,  1.04353798, -0.13855581, ..., -0.72484232,\n",
       "           1.11528119,  0.76363704],\n",
       "         [-0.62604102,  0.98323136,  0.72905469, ..., -2.19577012,\n",
       "          -0.4371583 ,  0.81451447]]]])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-c153b001-6143-4696-97aa-ac9543651296' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c153b001-6143-4696-97aa-ac9543651296' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2020-05-17T12:31:11.817960</dd><dt><span>arviz_version :</span></dt><dd>0.7.0</dd><dt><span>inference_library :</span></dt><dd>tensorflow_probability</dd><dt><span>inference_library_version :</span></dt><dd>0.7.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (chain: 1, draw: 1000, mu_dim_0: 4, theta_tilde_dim_0: 4, theta_tilde_dim_1: 8, trace_dim_0: 4)\n",
       "Coordinates:\n",
       "  * chain              (chain) int64 0\n",
       "  * draw               (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "  * mu_dim_0           (mu_dim_0) int64 0 1 2 3\n",
       "  * trace_dim_0        (trace_dim_0) int64 0 1 2 3\n",
       "  * theta_tilde_dim_0  (theta_tilde_dim_0) int64 0 1 2 3\n",
       "  * theta_tilde_dim_1  (theta_tilde_dim_1) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    mu                 (chain, draw, mu_dim_0) float64 5.696 2.455 ... 3.266\n",
       "    trace              (chain, draw, trace_dim_0) float64 2.985 5.002 ... 5.168\n",
       "    theta_tilde        (chain, draw, theta_tilde_dim_0, theta_tilde_dim_1) float64 -0.4164 ... 0.8145\n",
       "Attributes:\n",
       "    created_at:                 2020-05-17T12:31:11.817960\n",
       "    arviz_version:              0.7.0\n",
       "    inference_library:          tensorflow_probability\n",
       "    inference_library_version:  0.7.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_az.posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x149b35dd8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zOEBJajqDp93gkyyprQyeSpK6xROcJKmVPMFJklrJE5wkqZU8wUmSWskTXENExAER8ZWqWnB9RJweEY9e9HFJUl15gmuO3YGTgD0o5YIfAKdFxAMWeVDSSuzHaVH8HNwUIuIM4AJKn+35wO+Aw4GjKWWBZwO/AF6bmSdU69yHEizdJTO/0betBJ6Rmf864r4DuBJ4Y2a+a4XFfZK1EKecdwWHnvq9W+auPHSfnZy7UrM29HNwTtU1vUX14DahTNl13Qy3uarszU2mCd24nqb342zHzdZqF0p8iXJ6i+rBHQ7cAHxqxtuVZsZ+nBbJEdz0Vr0HFxEHUeaifFxm/mJW211t9uYm04RuXE/T+3G245rNE9z0VrUHV53cDgf2ysyvj3eoaoMmdON67MdpkTzBrb6Je3AR8beUlzyfmJlfnfWBSbNmP06L5AlulU3Rg3sl8EbgOcBFEbFN9aObMvPncztgaUr247QoXmSyGJP04F4K3IbyWbgr+25HzeMAJanp/BxcN/gkS2ore3CSpG7xBCdJaiVPcJKkVvIEJ0lqJU9wkqRW8gQnSWolT3ANERHPiIhvVLHTX0bE+RHxvEUflyTVlTOZNMc1lDkoL6TMdfkk4LiIuCoz/32hRyZNYO269U7hpblyBDeFiDgjIt4TEW+PiGsj4qqIOCgiNo2Id1ejrR9HxH5969wnIjIiHr5kWxkR+w7bV2Z+MTNPycwLM/O/M/MoSslgt/n9htJ8nHLeFex55Jc57NTvseeRX+aU865Y9CGphRzBTW/Vg6dVzfuxwPbAa2exzXkzbjp7TQqfLmUItV3qmr5yBDe9VQueRsSdIuIG4NfAZ4CXZeZ/TLtdabUZQtVqcAQ3vdUMnq6jpHVuD+wB/N+IuDQzvzCDbc9VXf/Ca7ImhU+XMoSq1eAJbnqrFjzNzJuBH1Xfnh8ROwD/ANT+BKfZa1L4dClDqFoNnuBW38TB0wE2Ajad7nCk1WcIVavBE9wqmyJ4+lrga8DFlJPaE4H9gAPneLjS3BhC1bx5gluMvwKOpQRP/xt4CfDlFda5PfAe4J7ATZTPwz03M0+c43FKUmMZPO0Gn2RJbWXwVJLULZ7gJEmt5AlOktRKnuAkSa3kCU6S1Eqe4CRJreQJTpLUSp7guKXr9v8WfRzLiYg7R8QJEfHz6nZCRGyx6OOSpLryBDeGUSdFnpOPAg8F9qL05h4KnLDA45GkWuv8CS4iPgj8OfDSqqqdVXV79+q/nxgRX4+IXwP/MyLuFxGfjIg1EfHLiPhmRDxpyTY3iYgjIuKyiPhVRFwcES/r+/mOEfGZiFgXEWsj4sSI2GaZY9yBclJ7YWaelZlnAy8CnhQR28/jcVGzrV23nnMvu46165oZRJVmofMnOOAg4GzgA5QZ/u8OXN7387cArwMeSJns+PbAfwCPBx4MnAz8W0Q8sG+dDwHPBf4W2AH438D1ABFxd8q8k98FHgE8rtrmpyJi2POxK3ADcFbffWcCv2QGIVW1yynnXcGeR36Zw079Hnse+WVOOe+KRR+StBCdn2w5M39ejc5uzMw1vfsjbpne7NDM/GzfKlcB3+r7/o0RsQ+wL3B4RDwA+Atgr8z8z2qZi/uW/2vgW5n59337ei5wLfBw4OsDDnMb4Krsmzi0L646dOSn4Z713rNXdX8/ue5Grrh+dUZTm/3BRpx64KNviYnu866v8vKTzl+VfU/qHltsxj3vvPmiD2NhDALPhyO4lX2j/5uIuF1E/FNEfD8irouIGygnpntXizyEEjU9fcj2Hgb8WUTc0LuxYcR4v2WOY9CEyTHkfnXY3be4LQ/Y+g4APGDrO3D3LW674COSFqPzI7gR/HLJ92+jvB/2d8APgRuB44FNqp8Pndm6shHwmWr9pX42ZJ01wFYREb1RXJQh5t2WWUfLWO2/mI/83EUc9YUfrsq+rrz+Jn74s3W3jOCuvP6mVdnvNPZ92L0aWydXfXmCK34NbDziso8Gjs/MkwEiYjPKyOui6uffpJzEHgP854D1vwk8E7gsM38z4j7PprxPtysb3ofbFbgdv/++nGrq4Mf/0ar9A37KeVfwjPeezXZ32ZzLrr2RNz/9f/CUh9xjVfYt1Yk9OCAijqG8dPh0ysUc1wJ/RnmZ8W6ZeXXfsicDDwD2B34D/B/KBSefyMz9q2VOAv6UcgHLNymR0vtk5gkRsS1wPvBVygUsVwH3pZz0XpGZ64Yc439U2zmAMko8Brg0M/cZ4Vf0Se6YtevWc/m1N3Gvu9zWarbazh7cCt5GGcV9n3LCufcyy/4tsBb4CuVqynOq/+73XMrn1t5JKW9/ELgTQGb+lHLyu5kywvse8G7gV9VtmGdTLm75LHBa9d/7jfbrqWu2usNmPGy7O3tyU6c5gusGn2RJbeUITpLULZ7gJEmt5AlOktRKnuAkSa3kCU6S1Eqe4CRJreQJjsYET18bEWdWiR4v+5ekFXiCG8OCg6ebAv8GvGOBx6CGsxOnLun8Ca4JwVOAzDwkM98OnDeHh0EdYCdOXdP5mUwi4k6UKbcuBP6huvsqYDfKXJTfBV4B/AhYB2xLmej4TOAm4FmU+Sj/R2ZeWG3zxGr9gygnpO2Ae2Xm8VXw9DvAcZQpvG4DvBHYGviTzLx5hePdF/iXzFypWtCv20/yAKvdg+tZzS7cUoM6cet/u+z/3GqlC804u3ATGfpvYedrAg0JnkpTG9SJu+TqpTUoqT06f4Ibwa2Cp5QR25OAu1NGYJsB364WGTl4OuBn98MT3KpY1F/Kq9mFW6qJnbh+NuM0Lk9wK6tD8FQtsZpduKXsxKlrPMEVdQ+eSlN7ykPuwaPuf1c7ceqMzl9FWbkUeER19eSWEbHc43IR8NSIeGhE/DHwYcpLlABk5g+BjwPHRsTTI+IPI2K3iOi1295NacOdFBGPjIj7RsTjIuKYiLjDsJ1GxL0jYmfgPtX3O1e320/xe6tj7MSpSzzBFU0Inh5GuSLzrdX351W3h6/860lS93T+YwId4ZMsqa0MnkqSusUTnCSplTzBSZJayROcJKmVPMFJklrJE5wkqZU8wVH/4Gn1AfTjquzOTdXXN0XEbRd9bJJUV07VNYaIuM2Cptd6IGUqsb+mzH+5A3AMcFfghQs4HjXM2nXrnaJLndP5EVwTgqeZ+Z+ZuX9mnpaZF2fmZygNuafP51FRmxg6VVd1fiaTpgVP+4771cALMvP+Iyze7Sd5gEUETxcVO61r6LQLAdN+xkznxuDpME0MnkbEvSm5nSNG+y3VZYZO1VWdP8GNoFbB04jYGjgN+Bxw5Gi/gpZaxF/Ti4qd1jV0asBU8+YJbmW1CZ5W79N9kfKy6X7Z9deXG2ZRsVNDp+oqT3BF7YOn1Xt3p1PyOn+Zmb8ddV11m6FTdVXnr6KsXEqNg6cRsS3wJWAN8HJgy4jYprqNemJWhxk6VRd5givqHjzdE3gA5eMMPwau7Lvda9RfUpK6pPMfE+gIn2RJbWXwVJLULZ7gJEmt5AlOktRKnuAkSa3kCU6S1Eqe4CRJreQJjkYETzeKiE9FxI8jYn1EXBkRH44I51uSpCE8wY0hIm6zwN1/kTLF1/aUDtx9gU8s8HhUU2vXrefcy65j7brVT/NIddL5E1xDgqc3Z+Y7MvOczLwsM88C3gzsUs2FKQHGTaV+nZ/JpInB04i4C/AeYLvM/JMRfs1uP8kDrEbwdBGB07rGTaF7gdNBjJ7OhcHTYZoUPI2ItwB/A2xOmQPzScOWVTcZN5U26PwJbgR1Cp6+lTLy2646hg9HxF524ca3Gn9JLyJwWte4KRg41erzBLey2gRPM/Nq4Grgooi4ALic0qdbWjNQDSwicGrcVNrAE1xR++DpAL0LhDadYhtqGeOm0gadv4qycin1Dp7uGhEvjYgHR8R2EfFY4MTquL865e+uljFuKhWe4Iq6B09volzE8kXKCfY4ynt+u2WmH3aSpAE6/zGBjvBJltRWBk8lSd3iCU6S1Eqe4CRJreQJTpLUSp7gJEmt5AlOktRKE53g+lIyW876gOalOt59h30/YPktq2V2X5UDlCTN1IonuHnWruex7Yj4YER8esCP7g6cOst9jXg8d46IEyLi59XthIjYYoLt3DsiTq0adFdHxDsjYpOV15SkburMXJT9KZxV9lHKzCh7UT5wfSxwArDPqBuIiI0pEzRfQ+nM3RX4EOUDjgfO+Hg7be269c7jKLXEsiO4YbXrvkUeHBFfi4gbI+IbEfHQJes/KiK+VP38ioh4T0TccbltR8TGEXFcRFwSETdFxA8j4lUrzA/Z29+hwPOAvfu2uXv1s5VektwlIs6NiPURcR7wyAHLjFXijogdKOWBF2bmWZl5NvAi4EkRsf1Kv0+fPYGdgP0y85uZ+TngVcABvcdT07OGLbXLSiO4g4A/4ta16/tU//0m4O+BK4GjgI9ExI6ZmdVExJ+ldMteANwFeAfwfsq8isO2vRFwBWXG/auARwDHUEYvx61wvG8Ddqj21Zvc+NoV1uk13j4DfIlygrxHdaz9y9wd+HJ1DH/HhhL3pyJiWIl7V+AG4Ky++86kJHgeBfxgpWPr284FmXl5332nUUoCD2N4e65WVqOi3TNJTXtQDfvlJ50/1jZWs1ptHVpa3rInuBFq16/PzNOr+w6jzGx/D+AnwCuBkzLz7X3r/TVwXkRslZlrB20b+B1wSN/3l1Yjw79khRNcZt4QETcBvxrzJclnU3puz8/MG4DvRsQbKS8l9kxS4t4GuKo/SFqd/NdWPxvVNty6FXc15bEaZztahjVsqV2mfQ/u233//dPq61aUE9zDgPtHxLP6lumdGe9HmZF/oIh4MWXUtx1wW8po6bIpj3U5OwDfrk5uPUuHG5OWuAdNdBxD7l/OsOUbM5Hyao44Jqlpz6KGbbVaqo9pT3D9wc7eP7Qb9X09FjhywHpD39yoTojvoLwMeBbwC+ClwFOnPNblrFThhslK3GuArSIieqO4KMPfuy2zzrDt/OmS+7akRFrH2U5nTFLTtoYttcsoJ7hxatf9vgnslJk/GnPbjwa+lpm3fHwgIu43xn4nOd7vA8+LiNtlZu81qT9ZsswkJe6zgdtT3kPrvQ+3K3A7fv99uVG287qIuGdm/qS67/GUfty5Y2xHy7CGLbXLKB/0vpTRa9f93lKtd3REPCQi7h8RT4qI966w7YuAh0bEXhHxgIh4PeVqy1FdCjwoIravtnmbEdb5KPBb4P0RsVNEPB547ZJlxi5xZ+YFlKjpeyPiTyJiV+C9wKczc9QLTKBcrPM94PjqsXwc8FbgfZn5izG2oxVYw5baY5ST1Ti161tk5reBP6Nccfkl4FuUqy77X1IbtO33Ah+nnHT+q1r/7YzufcAFwDeqbS59aW/Qsd4APAl4AGWk9jbK1aH9y0xS4oZyAcu3KCep06r/3q9/gYi4tPrYxLDj+x2wN3Aj5SrMk4B/Y/DLpZIkLHovXERsTvkIxF9l5olz2o1PsqS2suhdY4+hvOc4r5ObJHVS40ZwQy7T79krM7+yagfTHM16kiVpdENHcE2ci3LnZX7m3EqSJKCBIzhNxCdZUlv5HpwkqVsMng5f3uCpJDWYwdM5i9kFT4+qkkTrI+LSORyqJLVKZ16izMw1mbncB7Ln5aPAQynB0ydU/33CsmsMthElcnr87A6tu9auW8+5l13H2nXjJXUkNYfB0w3r1jl4SmYemJnvokxlpikYNpW6weApjQietsK8g6ejRk6nDZuuZtQUDJtKkzJ4WtQ9eKoZMmwqdYPB06IJwdPGm/dIZNTI6bRhU6OmUjMYPK12O8IyiwyeagSjRk4Nm0rdYPC0qHvwVDNk2FTqBoOnRd2Dp1SP387AtsAmEbFzddtknO2oMGwqtZ/BU+ofPK0cC5wHHEz50Pp51W3blX4/SeoiJ1tesDB4KknTcLLlGjN4Kklz0LgR3JDL9HsMng7WrCdZkkZn8FSS1C2NG8FpIj7JktrK9+AkSd1i8HT48gZPJanBDJ7OWcwgeBoRD67SPJdXCaEfRMQrx/jQvSR1ThMvMpnImHWBWfoo5QPse1HeCzuWUinYZ4xtPIzyofX9gB9TEkLvo0xCfcQsD7bt1q5b7xRdUqWq0HQAACAASURBVEcYPN2wbm2Dp5n5/sx8WWaekZkXZ+bHgPcATx91GzJ0KnWNwVMaGzy9I3DdFOvPzbzDpjB63LRfXUOnBk2l+TB4WjQqeFo9HvtXx60RGTqVusXgadGY4Gn10uZngHdk5smTbGPeVmNEMmrctJ+hU6lbDJ5Wux1hmYUHTyPigcDpwMcy89Xjrt8mo8ZN+xk6lbrF4GlR++BpROwIfBH4eGYePM66KgydSt1i8LSodfA0InaijNzOAI6IiG16t1G3ocLQqdQdBk9pRPD0GZT3Np9FuWK1/yZJGsDJlhcsDJ5K0jScbLnGDJ5K0hw0bgQ35DL9HoOngzXrSZak0Rk8lSR1S+NGcJqIT7Kktprte3BhD06SVHP24OYsZtODu1tEnBYRP42IX0Xpwr07Iu40r+OWpKbrzFWUmbkmM5f7vNq8fBR4KKUH94Tqv09Ydo1buxn4BKUh90eUiZb3oHzmTxNYu2495152HWvXjVckkNQc9uA2rFvnHtw1mXl0Zp6bmZdl5heAfwZ2G3Ub2sAunNQN9uBoXg8uIrYFnlYdb+3Nuw83bhtu2i4czK8Nt5StOGly9uCKRvTgIuJE4MmUhNCngeePuw3ZhZO6wh5c0ZQe3MHAPwLbA0dQRpkvmmA7q2reo5Bx23DTduHANpzUBPbgqt2OsMzCe3DVqHQNcGFEXAN8JSIOz8zLx91Wm4zbhrMLJ3WDPbii9j24AXp/SGw65XY6xy6c1A324Iq69+CeFBHPi4gHVY/V3sDRwDkr/AGhIezCSe1nD45G9ODWAy+mXMRzAeVl31OBJ670u0lSVzkX5YKFPThJmoY9uBqzBydJc9C4EdyQy/R77MEN1qwnWZJGZw9OktQtjRvBaSI+yZLayvfgJEndYvB0+PIGTyWpwQyezlnMIHi6ZHtbVumhRv2BIUmrrTMvUTY8eNrvA8B4bZeOM24qdZPB0w3r1jZ42re9g4DNGW9ml04zbip1l8FTmhE8jYiHUB7rXShTitXevEKn4wROp4mbGjWVms3gaVHr4Gl1Aj4RODAzr4iIRpzg6sC4qdRdBk+LugdP3wmcmZknj7HOws1rZDJO4HSauKlRU6nZDJ5Wux1hmUUGT/cA7hURz1tyvGsi4i2ZuTTt02rjBE6Nm0rdZfC0qHvwdE/KS6g9u1Dey9wdGG0o01HGTaXuMnha1Dp4mpkXZeZ3ezfgkupHF2bmOCPBTjJuKnWTwVMaETyVJI3JyZYXLAyeStI0nGy5xgyeStIcNG4EN+Qy/R6Dp4M160mWpNEZPJUkdUvjRnCaiE+ypLbyPThJUrcYPB2+vMFTSWowg6dzFjMKnvblf/pvL57HMUtSGzTxIpOJjFkXmKWPUj7AvhflvbBjKZWCfSbY1gFA/8n751MfXcesXbfeabukjjB4umHd2gdPgeurMnnvNvrU+DJ+KnWMwVOaETytHBURR1PmojwOOGbIfmtjXtFTGC98CvWLnxo6lebL4GlR6+Bp5RDgdMoJcw/K/JxbAoePuZ3OMn4qdYvB06LuwVMy8w19354fERtTige1PsHNc5QyTvgUjJ9KXWPwtNrtCMssMng6yNeAO0bE1l1N5owTPgXjp1LXGDwt6h48HWRnYD1w/ZTb6Qzjp1K3GDwtah08jYh9IuKAiHhQRNwvIl4AHEa5yGS5Fp2WMH4qdYfBUxoRPP0N8BLKiPDblCtQDwFesdLvJkld5WTLCxYGTyVpGk62XGMGTyVpDho3ghtymX6PwdPBmvUkS9LoDJ5KkrqlcSM4TcQnWVJb+R6cJKlbDJ4OX97gqSQ1mMHTOYsZBU+rbT0nIs6vkj5XR8Txsz5eSWqLJl5kMpGmB08j4mXAayiVhnMok1A7+++MGUSV2sPg6YZ1axs8rUZ8bwKem5kfzswfZeZ3MvPkUbehlRlEldrF4CmNCJ7uSZlAeuuI+D5lTsyvA6/IzItH3EatzSOMuppBVJhPFHUlRlOl4QyeFnUPnt6XcuJ/HfDyap+HAKdHxA6ZeeMY29IQBlGldjF4WtQ9eLoR5TF4WWZ+FiAink3pze0DnDTGtmppHiOR1QyiglFUqW4Mnla7HWGZRQZPr6y+fr93RzW6/ikj1h26yCCq1G0GT4u6B0/PrL5uTxkdExG3p3z0YZ4j204xiCq1i8HTotbB08y8CPgkcFRE/GlE7Ah8gPIy76DP/GlCBlGl9jB4SiOCp1TrnEP5sPqZwGbAHl5gIkmDOdnygoXBU0mahpMt15jBU0mag8aN4IZcpt9j8HSwZj3JkjQ6g6eSpG5p3AhOE/FJltRWs30PLuzBSZJqzh7cnMUMenARsX9fHWHpbZd5HbskNVlnrqLMzDWZudzn1eblo8BDKT24J1T/fcKya9zaSZQTdP/tw8AllM/7aUxr163n3MuuY+260WsDkprFHtyGdWvbg8vMm6oT9JqqkvALyiTLx6Zvoo7N7pvUDfbgaEQPbqlnUuaz/MCE66+qebTelhqn/da07pvNN2ky9uCKuvfglnohZT7LK1dcUrdi903qBntwRd17cBtWjNiJMjLce5L1F2E1RiDjtN/svkndYA+u2u0IyyyyB9fvhcDllAmfVRmn/Wb3TeoGe3BF3XtwAETEZpT3Ft855D0/jcDum9QN9uCKWvfg+uxbHcP7J1hXfey+Se1nD47G9OAADgBOy8wfj7CsJHWac1EuWNiDk6Rp2IOrMXtwkjQHjRvBDblMv8ce3GDNepIlaXT24CRJ3dK4EZwm4pMsqa18D06S1C0GT4cvb/BUkhrM4OmcRcRrI+LMiPhlREz8UmFE3DsiTq22c3VEvDMiNpnlsUpSm3TmJcoFBk83Bf6NJfmdcUTExpR5MO8A7EYpK+zLeB+A1wgMoUrtYfB0w7ozD54CZOYhVTLovJWOfxl7AjsB+2XmNzPzc8CrgAN6j6emZwhVaheDp8w1eDoruwIXZOblffedRhkdPgw4fY77nou6RVChGSFU46fS6AyeFvMKns7KNtw6r3M15bGaJpyqPoZQpXYxeFrMM3g6K8MuUGnkZ9zqFkEFQ6hS2xg8rXY7wjKTBE9nZQ23riJsSenezXvfjTVOBBUMoUptY/C0mFfwdFbOBl4XEffMzJ9U9z2ekuk5d5WPpbUMoUrtYvC0mEvwFG75/NrOVBfmRMTO1e32Y/xOn6X0546vHsvHAW8F3peZvxhjO1qBIVSpPQyeMvfg6WGUjwi8tfr+vOr28N4CKwVPM/N3wN7AjcCZwEmUz9YNerlUkoSTLS+cwVNJmoqTLdeYwVNJmoPGjeAMnk6kWU+yJI3O4KkkqVsaN4LTRHySJbWV78FJkrrF4Onw5Q2eSlKDGTydsxkGT4+qkkTrI+LSGR6iJLVSZ16ibHLwtLIR8CHg+KmPSLcwcCq1l8HTDevWOXhKZh6Yme+iTGWmGTBwKrWbwVMaETxthbpFTqcJnK5G3BQMnErTMHha1D14qjkwcCq1m8HTognB08arW+R0msCpcVOp/gyeVrsdYZlFBk81onEipwZOpXYzeFrUPXiqOTBwKrWbwdOi7sFTqsdvZ2BbYJO+7Wwyznb0+wycSu1l8JT6B08rx1brHUz50HpvO9uu9PtJUhc52fKCGTyVpKk42XKNGTyVpDlo3AjO4OlEmvUkS9LoDJ5KkrqlcSM4TcQnWVJb+R6cJKlbDJ4OX97gqSQ1mMHTOZtF8DQiHlyleS6vEkI/iIhXjvGhe0nqnCZeZDKRMesCs9QLnp7BhizQuB5G+dD6fsCPKQmh91EmoT5i+kPshrXr1jstl9QhBk83rFvb4Glmvj8zX5aZZ2TmxZn5MeA9wNMn3WbXGDeVusfgKY0Nnt4RuG6V9zmS1QibwurFTXvmHTk1birNlsHTolHB0+rx2L86bo3AuKnUPQZPi8YETyNie8po8x2ZefJq7HNcqzUSWa24aY+RU6lZDJ5Wux1hmYUHTyPigcDpwMcy89Wrsc86M24qaTkGT4vaB08jYkfgi8DHM/Pg1d5/0xk3lbrH4GlR6+BpROxEGbmdARwREdv0bqNuQ8ZNpa4xeEojgqfPoLy3+SzKFav9N0nSAE62vGBh8FSSpuFkyzVm8FSS5qBxI7ghl+n3GDwdrFlPsiSNzuCpJKlbGjeC00R8kiW11Wzfgwt7cJKkmrMHN2cxmx7c3SLitIj4aUT8KkoX7t0RcadZH68ktUVnrqLMzDWZudzn1eal14N7x0oLLuNm4BPAPpQCw/7AHpTP/GlCa9et59zLrmPtutGKBJKaxR7chnXr3IO7JjOPzsxzM/OyzPwC8M/AbpNus+vsw0ntZw+O5vXgImJb4GnV8dbeavThxmnDwfR9ONtwUv3Zgysa0YOLiBOBJ1MSQp8Gnj/vfbaVfTip/ezBFU3pwR0M/COwPXAEZZT5olXY71RWYzQyThsOpu/D2YaT6s8eXLXbEZZZeA+uGpWuAS6MiGuAr0TE4Zl5+Wrsv87GacOBfTipC+zBFbXvwQ3Q+0Ni04UeRUPZh5Pazx5cUfce3JMi4nkR8aDqsdobOBo4Z4U/ILQM+3BSu9mDoxE9uPXAiykX8VxAedn3VOCJK/1uktRVzkW5YGEPTpKmYQ+uxuzBSdIcNG4EN+Qy/R57cIM160mWpNHZg5MkdUvjRnCaiE+ypLbyPThJUrcYPB2+vMFTSWowg6dzFjMIni7Z3pZVeqhRf2BI0mrrzEuUDQ+e9vsAMHrXRYBxU6mLDJ5uWLe2wdO+/R8EbM54M7t0nnFTqZsMntKM4GlEPITyWO9CmVKsEeYVOx0ncDpN3NSwqdRcBk+LWgdPqxPwicCBmXlFRDTmBFcHxk2lbjJ4WtQ9ePpO4MzMPHmO+5iLeY1QxgmcThM3NWwqNZfB02q3IyyzyODpHsC9IuJ51fe9410TEW/JzKVpn9YbJ3Bq3FTqJoOnRd2Dp3tSXkLt2YXyXubuwGjDmA4zbip1k8HTotbB08y8KDO/27sBl1Q/ujAz5z16bAXjplL3GDylEcFTSdKYnGx5wQyeStJUnGy5xgyeStIcNG4EZ/B0Is16kiVpdAZPJUnd0rgRnCbikyyprXwPTpLULQZPhy9v8FSSGszg6ZzNKnjal//pv714lscqSW3SxItMJjJmXWCWesHTM9iQBZrUAUD/yfvnU26vU9auW+90XVKHGDzdsG7tg6fA9VWZvHcbfVr8jjN6KnWPwVOaETytHBURR1PmojwOOGaV9ju2eYVOlxo1fDpN9LSfAVSpOQyeFrUOnlYOAU4HbqDkc94ObAkcPuf9toLRU6l7DJ4WdQ+ekplv6Pv2/IjYmFI8qOUJbrVGIqOGT6eJnvYzgCo1h8HTarcjLLPI4OkgXwPuGBFbdzmZM2r41Oip1D0GT4u6B08H2RlYD1y/6ANpAqOnUvcYPC1qHTyNiH0i4oCIeFBE3C8iXkDpzB2Tmcu16NTH6KnULQZPaUTw9DfASyjvC36bcgXqIcArVvrdJKmrnGx5wcLgqSRNw8mWa8zgqSTNQeNGcEMu0+8xeDpYs55kSRqdwVNJUrc0bgSnifgkS2or34OTJHWLwdPhyxs8laQGM3g6ZzGj4Gm1redExPlV0ufqiDh+VscpSW3TxItMJtL04GlEvAx4DaXScA5lEmpn/Z0T46hS8xk83bBubYOnEbEFZRaY52bmhzPzR5n5ncw8edJtajjjqFI7GDylEcHTPSkTSG8dEd+nzIn5deAVmXnxHPc7N6sVRO0ZNYwK08VRZx1ENYAqTc7gaVH34Ol9KSf+1wEvr/Z5CHB6ROyQmTfOcd+dYxxVageDp0Xdg6cbUR6Dl2XmZwEi4tnAGmAf4KQ57nsuVntkMmoYFaaLoxpElerD4Gm12xGWWWTw9Mrq6/d7d1Sj658yYt2h60YNo4JxVKktDJ4WdQ+enll93Z4yOqbqyd2d+Y5sO8k4qtQOBk+LWgdPM/Mi4JPAURHxpxGxI/ABysu8gz7zpykZR5Waz+ApjQieQrkq9BzKh9XPBDYD9vACE0kazMmWFywMnkrSNJxsucYMnkrSHDRuBDfkMv0eg6eDNetJlqTRGTyVJHVL40ZwmohPsqS28j04SVK3GDwdvrzBU0lqMIOncxYzCJ5GxP59+Z+lt11mfcxdsnbdes697DrWrhutNCCpOZp4kclEGh48PYny4fJ+b6V88PwbEx9Zx51y3hUceur3bplz8tB9dnLOSalFlj3B9UVJ/zwiXlrd/Yd9izw4Io4A/pgyG8kLM/Obfes/ijJ7yS7AdcCngL/PzF8ss+3LKf23xwLbUOZefB/wtpWaa33BU/pGS4/JzDOq75+Rmf86ZN1dgKOBnSgzobxuwDI7Uk4sfwbcBHwBOHi5k2dmHlKtO/Tl0JVk5k3V/nrHsTmlIvBP2YCrhFar/TZO8w0m777NuvnWY/tNmq2VXqI8iJKN+QDlJb67U05APW8CXg08lDIbx0eiisX1BU8/BTwYeBrlEv/3r7Dt/uDpDpQ5If8BeP4Iv8/bKNN8fb5vm2ettFJf8PRiyhRar6621b9ML3j6XUqE9XHA7SnB09W+WOeZwO0oj50mNKj7Jqk9DJ4WdQ+eLvVC4NOZeeWKS9bAao1Mxmm+weTdN5tvUjMYPC3qHjy9RUTsBOwK7L0a+2uScZpvYPdNajuDp9VuR1hmkcHTfi+kvJS79KITjcnum9RuBk+LugdPAYiIzSjZnHeudMGNRrPVHTbzxCa1lMHTotbB0z77Vsfw/pUWlKSuM3hKY4KnAAcAp2Xmj0dYVpI6zcmWF8zgqSRNxcmWa8zgqSTNQeNGcAZPJ9KsJ1mSRmfwVJLULY0bwWkiPsmS2mq278HZg5Mk1Z09uDmqPt93XERcHBE3VV/fFBFjz+pbfZ7u1Kord3VEvDMiNpnHcUtSGzTxPbiJLKgH90DKrCp/DfyQMuflMcBdKVNujSQiNqZME3YNsFu1/ocoQ/MDZ3vI3bV23Xqn7ZJaZNkRXF+z7aV9Ben79C3y4Ij4WkTcGBHfqGb971//URHxpernV0TEeyLijsttOyI2rkY9l1Sjnh9GxKtGmUGlrwe3d982d69+ttJLkrtExLkRsT4izgMeOWCZHSPiMxGxLiLWRsSJEbHNsG1m5n9m5v6ZeVpmXpyZnwHeCDx9pd9liT0pnbr9MvObmfk54FXAAb3HU9M55bwr2PPIL3PYqd9jzyO/zCnneb2S1HQrjeAOAv4IuJANNeqrqKadosxM8vfAlcBRlB7cjpmZsaEH938oZYC7UCZRfj9lyqlh2+7vwV1Faa8dQxm9LJvLocxAskO1r/2q+65dYZ3+HtyXKCfIe1TH2r9Mrwd3HGXC5dtQTlafiog/GWNuyDtS4q/j2BW4IDP7W3ynUWrhDwNOH3N7C7Ma8dNxw6cwefy037xCqGAMVZqEPbhiVXpwEXFvysnxiDGODUrZfGmx4GrKYzV0BKnRDYqfXnL1L1dYS1Kd2YMr5t6Di4itKaOuzzE4IbSSYZf6N+ojAKsxEhk3fAqTx0/7GUKV6sUeXLXbEZaZuAdXvU/3ReC7lPfRxj0preHWk0ZvSbmAZTVbdI0wbvgUjJ9KbWQPrphbD6567+50Sn3gLzPzt2MeG5TR5Osi4p6Z+ZPqvsdTKgbnTrA9LWH8VGofe3DFXHpwEbEt5cKVNcDLgS0jYpvqNs5J+LOUE+Tx1WP5OEp+532Z+YsxtqNlbHWHzXjYdnf25Ca1hD045tqD27Pa5p8DP6Zcbdq73au3UKzQg8vM3wF7AzcCZwInAf/G4JdLJUk4F+XChT04SZqGPbgaswcnSXPQuBHckMv0e+zBDdasJ1mSRmcPTpLULY0bwWkiPsmS2sr34CRJ3WLwdPjyBk8lqcEMns5RzDZ4elSVJFofEZfO4XAlqVWaeJHJRJocPK1sRImc/jHlA+SagnFTqf0Mnm5Yt87BUzLzwMx8F2UqM03BuKnUDQZPaUTwtLHqGDite9y0n6FTaXIGT4u6B081Q8ZNpW4weFo0IXjaSHUMnBo3lbrB4Gm12xGWWWTwVMsYN3Bq3FTqBoOnRd2Dp5oh46ZSNxg8LeoePKV6/HYGtgU2iYidq9sm42xHhXFTqf0MnlL/4GnlWOA84GDKh9bPq27brvT7SVIXOdnygoXBU0mahpMt15jBU0mag8aN4IZcpt9j8HSwZj3JkjQ6g6eSpG5p3AhOE/FJltRWvgcnSeoWg6fDlzd4KkkNZvB0jmJGwdOIeHCV5rm82s4PIuKVY3zoXpI6p4kXmUyk4cHTh1E+tL4f5QPjj6B8oP02WCYYi6FTqTsMnm5Yt7bB08x8f2a+LDPPqLbzMeA9426n6wydSt1i8JTGBk9rHU6dd+h03MgpTB86nWfk1LCpNHsGT4tGBU+rx2P/6rg1IkOnUrcYPC0aEzyNiO0po813ZObJk25n3uY9Ihk3cgrTh06NnErNYvC02u0Iyyw8eBoRD6S05T6Wma+eZBttMW7kFAydSl1j8LSoffA0InaknCQ/npkHT7KNrjN0KnWLwdOi1sHTiNiJcpI8AziibxtDr+DUYIZOpe4weEojgqfPoLy3+awl27hypd9NkrrKyZYXLAyeStI0nGy5xgyeStIcNG4EN+Qy/R6Dp4M160mWpNEZPJUkdUvjRnCaiE+ypLbyPThJUrcYPB2+vMFTSWowg6dzFLMLnt4tIk6LiJ9GxK+ihE/fHRF3mtexS1LTdeYlysxck5nDPpA9L/3B052AA4HnUtJC47gZ+ASwDyUxtD+wB+VD7ZrA2nXrOfey61i7brzkjqTmMHi6Yd06B0+vycyjM/PczLwsM78A/DOw2zjbUWH4VOoGg6c0L3hazXH5tOp4G2fWMdRx46fThE/nGT0dxBCqNDmDp0UjgqcRcSLwZEoj79PA8yfZTtcZPpW6weBp0ZTg6cHAPwLbU06S7wBeNOG2FmbWo5Jx46fThE+NnkrNYfC02u0Iyyw8eFqNStcAF0bENcBXIuLwzLx8ku21xbjxU8OnUjcYPC1qHzwdoPeHxKYz2l5nGD6VusHgaVH34OmTIuJ5EfGg6rHaGzgaOGeFPyA0hOFTqf0MntKI4Ol64MWUi3guoLzseyrwxJV+N0nqKidbXrAweCpJ03Cy5RozeCpJc9C4EdyQy/R7DJ4O1qwnWZJGZ/BUktQtjRvBaSI+yZLaarbvwYU9OElSzdmDm6OYUQ9uyTa3rMoMjfoDQ5JWWxPfg5vImJMvz0p/D+6HlDkvjwHuCrxwwm1+ADgf2HYWB9gFa9etd9YSqYPswW1Yt7Y9uL79HwRszngffO80229Sd9mDoxk9uIh4COWx3oUyO0qtzbr5BuN336AZ7Tebb9J82IMrat2Dq07AJwIHZuYVEVH7E1xd2H6TusseXFH3Htw7gTMz8+Qx11uYeYxKxu2+ge03qcvswVW7HWGZRfbg9gDuFRHP622y+romIt6SmUvLB600bvcNbL9JXWYPrqh7D25PykuoPbtQ3svcnXJ1poaw/SZ1lz24otY9uMy8KDO/27sBl1Q/ujAzlx09yvab1FX24GhED06SNCbnolywsAcnSdOwB1dj9uAkaQ4aN4Ibcpl+jz24wZr1JEvS6OzBSZK6pXEjOE3EJ1lSW/kenCSpWwyeDl/e4KkkNZjB0zmKGQZP+/I//bcXz+O4JakNmniRyURaEjw9AOg/ef982gPsGuOnUncYPN2wbu2Dp8D1mbmm7zb61Pgyfip1jMFTmhE8rRwVEUdT5qI8DjhmjP0uxDzCp7D68VOYXwDV4Kk0HwZPi1oHTyuHUKoEN1DyOW8HtgQOn2BbnWT8VOoWg6dF3YOnZOYb+r49v6oRvJaan+DmNTpZ7fgpGECVmsbgabXbEZZZZPB0kK8Bd4yIrbuYzDF+KmklBk+LugdPB9kZWA9cP6PttZ7xU6lbDJ4WtQ6eRsQ+EXFARDwoIu4XES8ADqNcZDKsRacBjJ9K3WHwlEYET38DvITyvuC3KVegHgK8YqXfTZK6ysmWFywMnkrSNJxsucYMnkrSHDRuBDfkMv0eg6eDNetJlqTRGTyVJHVL40ZwmohPsqS28j04SVK3GDwdvrzBU0lqMIOncxQzDJ5W23tORJxfJX2ujojjZ33MktQWTbzIZCJND55GxMuA11AqDedQJqF25t8ZM4gqtYfB0w3r1jZ4GhFbUGaBeW5mfjgzf5SZ38nMk8fZjpZnEFVqF4OnNCJ4uidlJLh1RHyfMifm14FXZObFY26rtuYRRx0njDpNEHVeMVQwiCpNyuBpUffg6X0pJ/7XUSZtvpYqgBoRO2TmjWNuTwMYRJXaxeBpUffg6UaUx+BlmfnZanvPplQK9gFOGnN7tTSPkco4YdRpgqjGUKX6MXha7XaEZRYZPL2y+vr93h3V6PqnjFh36KpxwqgGUaV2MXha1D14emb1dXvK6JiIuD3low/zHNl2ikFUqV0Mnha1Dp5m5kXAJ4GjIuJPI2JH4AOUl3kHfeZPEzKIKrWHwVMaETyFclXoOZQPq58JbAbs4QUmkjSYky0vWBg8laRpONlyjRk8laQ5aNwIbshl+j0GTwdr1pMsSaMzeCpJ6pbGjeA0EZ9kSW3le3CSpG4xeDp8eYOnktRgBk/nKGYUPI2I/fvyP0tvu8zr+CWpyZp4kclEGh48PYny4fJ+b6V88Pwb0x9mNxk3ldrN4OmGdWsbPM3MmzJzTe9GmYB6H+DYCSZuFsZNpS4weEojgqdLPRO4HWU+ykaYR8x0qdWKm8L8AqfGTaXZMXha1D14utQLgU9n5pUrLqmBjJtK7WfwtKh78LR/OzsBuwJ7T7qNRViNkclqxU3BwKnUBAZPq92OsMwig6f9Xghczq0vOuk846aS+hk8LeoePO1tazPKe4vvHOM9Pw1g3FRqP4OnRa2Dp332rY7h/ROsqyWMm0rtZvCUzgT7IQAAEu5JREFUxgRPAQ4ATsvMH4+wrCR1mpMtL1gYPJWkaTjZco0ZPJWkOWjcCG7IZfo9Bk8Ha9aTLEmjM3gqSeqWxo3gNBGfZEltNdv34MIenCSp5uzBzVFEbBQRn4qIH1eVgisj4sMRMfaUGRFx74g4NSJ+GRFXR8Q7I2KTeRy3JLVBZ66irFIzwz6vNk9fpMyAsj0lk3Nf4BPjbKD6UPhngDsAu1Emnt6X8T4fqBWsXbeecy+7jrXrRisSSKo3e3Ab1p1HD+7mzHxHZp6TmZdl5lnAm4Fdqmm3RrUnsBNlHstvZubngFcBB/QeT03HPpzUPvbgWL0eXETchZLm+VpmjjNM2BW4IDMv77vvNGBTSuXg9DG2VTvzaMWN04aD+vbhRmFDThrMHlwx1x5cRLwF+Btgc+AcyrRg49iGWxcLrqY8VkNHkBqdfTipfezBFfPuwb2VcnLejjKi/XBE7DVmNmfYso3/CMA8RiDjtOHAPpzURvbgqt2OsMzEPbjMvJoy4rooIi6g9NweDYw668oabj1p9JaULNCy++6qcdpwYB9OaiN7cMXcenAD9P4A2HSMdc4GXhcR98zMn1T3PZ5SMTh3imNRxT6c1D724Ip59eB2jYiXRsSDI2K7iHgscGJ1jF8d43f6LCXPc3z1WD6O8rLn+zLzF2NsR8uwDye1iz045tqDu4lyxegXKSfu4yjvW+7WfxVlrNCDy8zfAXsDNwJnAicB/8bgl0slSTgX5cKFPThJmoY9uBqzBydJc9C4EdyQy/R77MEN1qwnWZJGZw9OktQtjRvBaSI+yZLayvfgJEndYvB0+PIGTyWpwQyezlHMNnh6VJUkWh8Rl87hcCWpVTrzEmWTg6eVjYAPAcfP7tBk5FRqL4OnG9atc/CUzDwwM99FmRFFM2DkVGo3g6c0InjaCvMIm/aMGziF6SKnqx04NWoqjc/gaVH34KnmwMip1G4GT4smBE8bb56jkHEDpzBd5NTAqVR/Bk+r3Y6wzCKDp1rBuIFTMHIqtZ3B06LuwVPNgZFTqd0MnhZ1D55SPX47A9sCm0TEztVtk3G2o99n5FRqL4On1D94WjkWOA84mPKh9fOq27Yr/X6S1EVOtrxgYfBUkqbhZMs1ZvBUkuagcSO4IZfp9xg8HaxZT7Ikjc7gqSSpWxo3gtNEfJIltZXvwUmSusXg6fDlDZ5KUoMZPJ2jmFHwtPqg+IkRcXmVEPpBRLxyjA/dS1LndOYfyIYHTx9G+dD6fsBOlAmbDwFePbvDbD/jplK3GDzdsG5tg6eZ+f7MfFlmnpGZF2fmx4D3UE6YGoFxU6l7DJ7S2ODpHYHrptzGzMwzZrqcUUOn08RNYfUCp4ZNpdkxeFo0KnhaPR77V8etERg3lbrH4GnRmOBpRGxPGW2+IzNPHnf9eVnUyGPU0Ok0cVMwcCo1kcHTarcjLLPw4GlEPBA4HfhYZnqBCaOHTo2bSt1j8LSoffA0InakXJH58cw8eIr9d5JxU6l7DJ4WtQ6eRsROlJHbGcAREbFN7zbqNmTcVOoag6c0Inj6DMp7m8+iXLHaf5MkDeBkywsWBk8laRpOtlxjBk8laQ4aN4Ibcpl+j8HTwZr1JEvS6AyeSpK6pXEjOE3EJ1lSW/kenCSpWwyeDl/e4KkkNZjB0zmK2QVP7xYRp0XETyPiV1HCp++OiDvN69glqek68xJlw4OnN1fr7ENJDO0P7EH5ULsmZABVajeDpxvWrXPw9JrMPDozz6228wXgn4HdRt2Gfp8BVKn9DJ7SvOBpRGwLPK063tpa7QjqqPFTqHcA1eipNBsGT4tGBE8j4kTgyZRG3qeB50+yHRlAlbrA4GnRlODpwcA/Ut7PO4IyynzRmNtYNas9Ehk1fgoGUKUuMHha7XaEZRYePK1GpWuACyPiGuArEXF4Zl4+znbaatT4KRhAlbrA4GlR++DpHLfTSQZQpfYzeFrUPXj6pIh4XkQ8qHqs9gaOBs5Z4Q8ILcMAqtRuBk9pRPB0PfBiyknxAsrLvqcCT1zpd5OkrnKy5QULg6eSNA0nW64xg6eSNAeNG8ENuUy/x+DpYM16kiVpdAZPJUnd0rgRnCbikyyprWb7HlzYg5Mk1Zw9uDmKGfXglmxzy6rM0Kg/MCRptXXmKsqG9+D6fQAYfdp7AbbfpC6yB7dh3dr24Pr2fxClSDDOB987z/ab1E324GhGDy4iHkJ5rHehzLhSW/PuwI3TfYPp2m/z7L6B7TdpnuzBFbXuwVUn4BOBAzPzioio9Qmubmy/Sd1kD66oew/uncCZmXnyiMsv1LxHJeN032C69pvdN6m57MFVux1hmUX24PYA7hURz1tyvGsi4i2ZubR80GrjdN/A9pvUVfbgirr34PakvITaswvlvczdgdGHMh1l+03qJntwRa17cJl5UWZ+t3cDLql+dGFmLjt6VGH7Teoee3A0ogcnSRqTc1EuWNiDk6Rp2IOrMXtwkjQHjRvBDblMv8ce3GDNepIlaXT24CRJ3dK4EZwm4pMsqa18D06S1C0GT4cvb/BUkhrM4OkcxQyDp335n/7bi+dx3JLUBp15ibIlwdMDKCfq3u1DszjALjF8KnWHwdMN69Y+eApcX52oe7fRp8WX4VOpY5a9ijIi7gT8B7eOku4GnE6ZSqs/eHpvoD94ejYlD/MpNgRPr8jMfZfZ9kbA6ykvJ/YHT1+Rmcv24CLi9pTpsH4veJqZv46IBJ6Rmf9aLXvL91Vv7RLKlGL/yIbg6QOBx2TmGVGCp9+ptv9BNgRPtwbGCZ6+B9guM5dO5rzSuklJEm1aHetxwDGj7JcaXEU5jwjqLMKn6387ysM3//DpUoZQpZFN9jk4g6f1CJ5WDqH8UXEDJZ/zdmBL4PAJttVJhk+lbjF4WtQ9eEpmvqHv2/MjYmNK8aARJ7h5jEgMn0pajsHTarcjLLPI4OkgXwPuGBFbdzWZY/hU0nIMnhZ1D54OsjOwHrh+yu10huFTqVsMnha1Dp5GxD4RcUBEPCgi7hcRLwAOo1xksoiPPjSW4VOpOwye0ojg6W+Al1DeF/w2cBDlopNXrPS7SVJXOdnygoXBU0mahpMt15jBU0mag8aN4IZcpt9j8HSwZj3JkjQ6g6eSpG5p3AhOE/FJltRWvgcnSeoWg6fDlzd4KkkNZvB0jmKGwdNqe8+JiP+/vbuPlaOqwzj+fQghIqUSbLD4h5ZGxWJrQEGD8q5VRIkJEHwBpSqgYnhRCMZAgEQjMSFgJWqiBAmpSWuCiVCkLVYLphSkmCZiU8C0IEIBa9RaSy2Q4x+/ue3cvTv7cru787LPJ5m0d/fM9Jx7Ont23s6zIdvWNkl3DrrOZmZNMTanKOseeCrpMmLS5puA+cTjBb8aXDVtgkNRzZrBgad7161s4KmkQ4hZYD6fUlqSUvpLSulPKaW7et2G9cahqGbN0e0xgcuBdzA1lHRO9vcbmRx4+nNJ+cDTVUQ8zIXsDTy9nZi+qmjb+xG3+5/L5MDTf9AlD444uplHS+Bpl3VQBJ7eS0wpdgF7A0/zZQ4HHszqcBV7A0/vltRP4Ol5xIPd/RwefISYQPpNkjYSc2L+gQiB3dzHdkozjMDTXgwiFPWKZRt6WndYoagOPzWbHgeehqoHns4lBv5rgSuyf/M64HeS5qWUdva5PSvgUFSz5nDgaah64Ol+xO/gspTSKgBJ5wEvAGcCy3rcTmnKOgpxKKrZ+HLgafbP9lCmzMDTrdmfG3Pb/Lek5+kx3WFcORTVbHw58DRUPfB0bfbnkcTRMZJmEI8+DPPIduw4FNWsORx4GiodeJpSepJ4JGCxpA9KOgr4GXGat90zf7YPHIpq1gwOPKUWgacQd4U+TDysvhZ4HfAh32BiZtaeJ1sumRx4ama2LzzZcoU58NTMbAhqdwRXcJv+BAeetlevTjYz650DT83MbLzU7gjOpsWdbGZN5WtwZmY2Xhx4WlzegadmZjXmwNMh0oACTyUtysX/tC7HDav+ZmZ1NjanKGseeLqMGKDzyxJgC/FAu/XJoaZmzefA073rVjbwNKX0cjZAv5DFAG0nUgRu6yORwDIONTUbDw48pRaBp63OBQ4i5qOsrFGFnI4y1HTCsMJNwQGnZoPiwNNQ9cDTVhcDy1NKW7uWtCkcamo2Hhx4GqoeeLqHpHcBxwMf73fdURvVkcgoQ00nONzUrPoceJr9sz2UKTPwNO/ibP0V01i3kRxqambtOPA0VD3wFIDsxpTPAT/o5ZqftedQU7Px4MDTUOnA05xzsjrcPo11LcehpmbN58BTahN4CnARsDKl9NceypqZjTVPtlwyOfDUzGxfeLLlCnPgqZnZENTuCK7gNv0JDjxtr16dbGbWu8IjuDoOcG/r8PZzKaX+H2pqOEkrgH1JfphFPObQJG5TPbhN9VBmm7allE5v90btBjgbPUnrU0rHll2PQXKb6sFtqoeqtsnX4MzMrJE8wJmZWSN5gLNe/KTsCgyB21QPblM9VLJNvgZnZmaN5CM4MzNrJA9wZmbWSB7gbA9Jh0q6VdImSS9LelbSjyW9sct6iySlNktpMxlLukTSFkm7JD0m6cQu5RdIeiBr93OSrlMu2bdMkr4l6VFJ2yX9XdI9kuZ3WWdOQZ+0fV5o1CTd0KZuHUOKq9xHsGdO2Xa/83sLyleujySdJOnu7PebJC1qeV9Z3z2f9cOaLKOy23ZPzvbDXZI2KzI/h84DnOW9mUhkvxpYAJxPTJjdyzRiO4HD80t+QulRUmQKLga+CxxD5AreJ6ntROGSZgL3ExOBHwdcRiTSf2MkFe7uFOBHwAeA04jki99IOrSHdU9ncr/8dkh1nI4nmFy3BUUFa9BHEPXKt+c9xCxCv+iyXpX6aAbwOHA5MVl8q6uBK4FLifa+BNxflKgCIOkI4NfEfngMMen+rZLOHmzV20gpefFSuABnEAkKMzuUWQTsKLuuufo8Avy05bWngBsLyn+VCNY9MPfatUQwr8puT5v6zgBeA87sUGYO8eF6bNn1LajfDcDjfZSvVR9l9bsG+Bfw+pr20Q5gUe5nAVuBa3KvHQj8B/hyh+18D3iq5bXbgHXDboOP4KybmUQc0M4u5Q6U9Iykv0laLumYEdRtCkkHAO8FVrW8tYo4AmrneOD3afI0byuJI9o5g67jABxMnH35Zw9lfynpJUlrJZ0z5Hr1a252KmyLpKWS5nYoW6s+yk6dfglYklLqtu9UuY/yjgBmk9u3sv54kOJ9C6LvWvfHlcCx6i2vc9o8wFkhSYcA3yaOhl7tUPQJ4IvAJ4HPALuAtZLePvxaTjGLSHR/seX1F4mds53ZBeUn3quaxcAGYF2HMjuAq4gU+jOA1URY7/nDr15PHiGO/D9G5BzOBh7qcL23bn20kBgQbutQpup91Gri99zPvjWxXrt19mff5sjtav9hbtyqQdJ3mJpQ3urUlNKa3DoHAfcQp4Cu7rRiSmkduQ9bSQ8RH8CXEtdKytD6gKfavNatfLvXSyXpZuAE4ISU0mtF5VJK25gcFLxe0iyiL5cMt5bdpZTuy/8s6WFgM3ABcHPRai0/V7KPMhcBj6aUNhQVqHofddDvvlW0TrvXB8oD3Hj4Pt13mD0p4ZJmEBeFAT6R+rxZJKX0mqT1REL6qG0jrk+1fqM8jKnfIie8UFCeDuuMnKRbgE8TX0Y2T2MTjwBfGGytBiOltEPSnyn+P1OLPgKQdBhxNuNr01i9sn1E9AFEPzybe73TvjWxXru+e5UIex4an6IcAymlbSmlTV2WnQDZ3VAriNN8Z6SUOuXvtZVdf3g3cUF6pFJKu4HHiFNEeQuJu7jaWQec2PJYw0LgeeDpQddxOiQtBj4LnJZS2jTNzRxNCX3Si+x3/06K61f5PspZRFy3XjqNdSvbR8AWYrDas29l/XEixfsWRN99uOW1hcD6lNIrg67kJGXfqeOlOgtx88I6YOKb9OzcckCu3GpydyQC1wMfBeYSO+jtwCvA+0pqx6eA3cCFwDzimtUO4K3Z+zcCq3Pl30DsuEuB+cBZxB17V5bdJ1n9fpjV57SWPpmRK9PapguIAXEecCRxrWc38PWy25PV7ybgZOI61fuB5Vkba9lHuXoKeJKWu3jr0kfEHbpHZ8tO4Lrs72/J3v9m9ns/K+uHpcSXjINz27gTuDP38xHAf4kzSfOy/XI3cPbQ21P2fwgv1VmI561SwXJKrtzTwB25n28BniG+tb5E3CF1fMltuSSr5/+II7qTcu/dATzdUn4BcTfYLuIb9PVU5PbzDn1yQ1Gbsg/PjdkHy3ZgPXB+2W3J1W/ig3E3cZ33LuCouvZRro6nZn0z5ctdHfqow2fAHdn7Ih7x2Jr1wwPA/JZtrAHWtLx2MvDHbH/cAnxlFO3xZMtmZtZIvgZnZmaN5AHOzMwayQOcmZk1kgc4MzNrJA9wZmbWSB7gzMyskTzAmZlZI3mAMzOzRvIAZ2ZmjfR/43zRL005OE4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(data_for_az)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:stan2tfp-dev]",
   "language": "python",
   "name": "conda-env-stan2tfp-dev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}