03-Sampling-and-MCMC.ipynb

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   "cell_type": "markdown",
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    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "# 3. Sampling and Markov Chain Monte Carlo (MCMC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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   "source": [
    "##### Bibliography:\n",
    "\n",
    "- [Textbook](https://press.princeton.edu/books/hardcover/9780691198309/statistics-data-mining-and-machine-learning-in-astronomy) Chapter 5\n",
    "- [Gordon Richard's notebooks](https://github.com/gtrichards/PHYS_T480)\n",
    "- [Jake VanderPlas's workshop \"Bayesian Astronomy\"](https://github.com/jakevdp/BayesianAstronomy)\n",
    "- [Jake VanderPlas's blog \"Frequentism and Bayesianism: A Practical Introduction\"](http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/)\n",
    "\n",
    "\n",
    "##### Additional reading:\n",
    "- [Jake VanderPlas: \"How to be a Bayesian in Python\"](http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/)\n",
    "- [Thomas Wiecki: \"The Inference Button: Bayesian GLMs made easy with PyMC3](https://twiecki.io/blog/2013/08/12/bayesian-glms-1/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some imports..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hack to fix pymc3 incompatibility with more recent versions of numpy\n",
    "\n",
    "import numpy\n",
    "\n",
    "def patch_asscalar(a):\n",
    "    return a.item()\n",
    "\n",
    "setattr(numpy, \"asscalar\", patch_asscalar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from scipy import stats, integrate\n",
    "from scipy.stats import norm, cauchy\n",
    "\n",
    "from astroML.plotting import hist, plot_mcmc\n",
    "from astroML.plotting.mcmc import convert_to_stdev\n",
    "from astroML.utils.decorators import pickle_results\n",
    "\n",
    "import pymc3 as pm\n",
    "import emcee\n",
    "\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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   "source": [
    "## Bayes is all about posteriors\n",
    "\n",
    "* Bayesian inference is all about deriving ***posteriors***. There's no more \"certainty\", it's all about the probability distributions!\n",
    "\n",
    "* For dimensions $\\gtrsim 3$, brute-force computation of posteriors (i.e., computing all values on a grid) becomes infeasible. We need a smarter method, one that will only compute the values of the posterior where it's appreciably non-zero.\n",
    "\n",
    "* What do we do???"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The answer is sampling!\n",
    "\n",
    "* Instead of calculating the value of the posterios for every possible parameter, we sample it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def twoD_gauss_with_grid():\n",
    "    import numpy as np\n",
    "    from matplotlib import pyplot as plt\n",
    "    from matplotlib.patches import Ellipse\n",
    "    from astroML.stats.random import bivariate_normal\n",
    "\n",
    "    mean = np.array([0, 0])\n",
    "    sigma_1 = 2\n",
    "    sigma_2 = 1\n",
    "    alpha = np.pi / 4\n",
    "\n",
    "    #------------------------------------------------------------\n",
    "    # Draw 10^5 points from a multivariate normal distribution\n",
    "    #\n",
    "    np.random.seed(0)\n",
    "    x, cov = bivariate_normal(mean, sigma_1, sigma_2, alpha, size=100_00,\n",
    "                              return_cov=True)\n",
    "\n",
    "    sigma_x = np.sqrt(cov[0, 0])\n",
    "    sigma_y = np.sqrt(cov[1, 1])\n",
    "    sigma_xy = cov[0, 1]\n",
    "\n",
    "    #------------------------------------------------------------\n",
    "    # Plot the results\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    # plot a 2D histogram/hess diagram of the points\n",
    "    H, bins = np.histogramdd(x, bins=2 * [np.linspace(-9, 9, 101)])\n",
    "    ax.imshow(H, origin='lower', cmap=plt.cm.binary, interpolation='nearest',\n",
    "              extent=[bins[0][0], bins[0][-1], bins[1][0], bins[1][-1]])\n",
    "\n",
    "    # draw 1, 2, 3-sigma ellipses over the distribution\n",
    "    for N in (1, 2, 3):\n",
    "        ax.add_patch(Ellipse(mean, N * sigma_1, N * sigma_2,\n",
    "                             angle=alpha * 180. / np.pi, lw=1,\n",
    "                             ec='k', fc='none'))\n",
    "\n",
    "    kwargs = dict(ha='left', va='top', transform=ax.transAxes)\n",
    "\n",
    "    ax.text(0.02, 0.98, r\"$\\sigma_1 = %i$\" % sigma_1, **kwargs)\n",
    "    ax.text(0.02, 0.93, r\"$\\sigma_2 = %i$\" % sigma_2, **kwargs)\n",
    "    ax.text(0.02, 0.88, r\"$\\alpha = \\pi / %i$\" % (np.pi / alpha), **kwargs)\n",
    "\n",
    "    ax.text(0.15, 0.98, r\"$\\sigma_x = %.2f$\" % sigma_x, **kwargs)\n",
    "    ax.text(0.15, 0.93, r\"$\\sigma_y = %.2f$\" % sigma_y, **kwargs)\n",
    "    ax.text(0.15, 0.88, r\"$\\sigma_{xy} = %.2f$\" % sigma_xy, **kwargs)\n",
    "\n",
    "    ax.set_xlabel('$x$')\n",
    "    ax.set_ylabel('$y$')\n",
    "\n",
    "    import matplotlib.ticker as plticker\n",
    "    loc = plticker.MultipleLocator(base = float(bins[0][1]-bins[0][0]))\n",
    "    plt.gca().xaxis.set_minor_locator(loc)\n",
    "    plt.gca().yaxis.set_minor_locator(loc)\n",
    "\n",
    "    plt.grid(visible=True, which='minor')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[-2.77770063, -2.21179288],\n",
       "       [-2.96869531,  0.20040625],\n",
       "       [-1.95008602, -3.33216565],\n",
       "       ...,\n",
       "       [ 1.24049979,  1.62468751],\n",
       "       [ 0.19463384,  0.11365702],\n",
       "       [ 1.72844543,  1.26666448]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = twoD_gauss_with_grid()\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image above has the same number of samples (which were binned and visualized as the image) and grid points.\n",
    "\n",
    "Notice how nearly all of the grid points are outside of the area where $p(x)$ is appreciably different from zero. If we computed an integral, less than $\\sim 10\\%$ of points would cover the area that actually contributes appreciably to the sum.\n",
    "\n",
    "Contrast this to sampling from $p(x)$, where nearly all samples are concentrated in the area where the value of $p(x)$ is relatively high (close to the center). This makes computing the integral from these points over 10x more more efficient (more accurate for the same amount of work) than doing it on a grid!\n",
    "\n",
    "This difference becomes even more drammatic as the dimensionality of the problem grows (e.g., imagine what this would look like in 3D, 4D, etc...)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do we get the samples? Markov Chain Monte Carlo.\n",
    "\n",
    "MCMC is a technique to efficiently draw **representative** samples from a distribution. Note: **it's not the only one**!\n",
    "\n",
    "We will not go into the details of *how* it works here, but review the software packages we can use to draw samples using MCMC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##  Markov Chain Monte Carlo with Python\n",
    "\n",
    "We will use two popular python modules:\n",
    "PyMC and emcee. Quoting \n",
    "[Jake VanderPlas' blog:](http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/)\n",
    "\n",
    "### emcee\n",
    "\n",
    "* The emcee package (also known as MCMC Hammer, which is in the running for best Python package name in history) is a Pure Python package written by astronomer Dan Foreman-Mackey. It is a lightweight package which implements a fairly sophisticated Affine-invariant Hamiltonian MCMC. Because the package is pure Python (i.e. it contains no compiled extensions) it is extremely easy to install; with pip, simply type at the command-line \"pip install emcee\".*\n",
    "\n",
    "* Emcee does not have much specific boilerplate code; it simply requires you to pass it a Python function which returns a value proportional to the log-posterior probability, and returns samples from that posterior.*\n",
    "\n",
    "### PyMC3\n",
    "\n",
    "* The PyMC3 package has many more features than emcee, including built-in support for efficient sampling of common prior distributions. PyMC3 relies on the Theano library to construct probability distributions and then access the gradient in order to implement cutting edge inference algorithms. PyMC3 also provides several samplers. Unlike earlier version, it doesn't rely on complied fortran libraries, can be easily installed either by pip or conda.\n",
    "\n",
    "More details about PyMC3 are available from [the pyMC3 User Guide](https://docs.pymc.io/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Markov Chain Monte Carlo with emcee\n",
    "We will first use emcee to get a 2-dimensional posterior pdf \n",
    "for location and scale parameters using a sample drawn from \n",
    "Cauchy distribution.\n",
    "\n",
    "Problem statement: 10 samples were drawn from a Cauchy distribution with unknown $\\mu$ and $\\gamma$. Estimate the posterior of $\\mu$ and $\\gamma$, assuming a uniform prior on $\\mu$ and a $\\frac{1}{\\gamma}$ prior on $\\gamma$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating the mock dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(44)\n",
    "mu_0 = 0\n",
    "gamma_0 = 2\n",
    "xi = cauchy(mu_0, gamma_0).rvs(10)\n",
    "plt.hist(xi, bins='fd')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use Bayes' theorem to find the posterior:\n",
    "\n",
    "$$p(\\mu,\\gamma \\mid {x_i}) \\propto p({x_i} \\mid \\mu, \\gamma) \\, p(\\mu, \\gamma) = p({x_i} \\mid \\mu, \\gamma) \\, p(\\mu) \\, p(\\gamma) \\\\\n",
    "= \\left[\\prod_i \\frac{1}{\\pi \\gamma} \\left( \\frac{\\gamma^2}{\\gamma^2 + (x_i-\\mu)^2} \\right) \\right]\\times 1 \\times \\frac{1}{\\gamma} \\\\\n",
    "= \\left[\\prod_i \\frac{1}{\\pi} \\left( \\frac{\\gamma}{\\gamma^2 + (x_i-\\mu)^2} \\right) \\right]\\times  \\frac{1}{\\gamma}$$\n",
    "\n",
    "This can still be solved using the brute force computation on a grid, resulting in:\n",
    "\n",
    "<img src=\"figures/Lcauchy.jpg\" alt=\"Drawing\" style=\"width: 600px;\"/>\n",
    "\n",
    "(this is Figure 5.10 in the textbook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`emcee` actually expects to receive a function returning the natural log of the posterior, so we let's find the natural log:\n",
    "\n",
    "$$ \\log p(\\mu, \\gamma \\mid {x_i}) = \\left[\\sum_i \\log \\left(\\frac{\\gamma}{\\gamma^2 + (x_i-\\mu)^2}\\right)\\right] - \\log \\gamma + {\\rm const} \\\\\n",
    "= \\left[\\sum_i \\log \\gamma - \\log \\left(\\gamma^2 + (x_i-\\mu)^2\\right)\\right] - \\log \\gamma + {\\rm const}$$\n",
    "\n",
    "We only need to know the unnormalized posterior, which is equivalent to dropping a constant in the log posterior that doesn't depend on $\\mu$ or $\\gamma$. We'll define separate functions for the log likelihood and the log prior, which isn't required, but will make it easier to change the prior if we want to.\n",
    "\n",
    "We also know that $\\gamma$ has to be positive, so our prior (and thus our posterior) is zero when $\\gamma$ is negative. `emcee` [wants us](https://emcee.readthedocs.io/en/stable/user/faq/) to return `-np.inf` in these cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def myLogPrior(mu, gamma):\n",
    "    if gamma > 0:\n",
    "        return -np.log(gamma)\n",
    "    else:\n",
    "        return -np.inf\n",
    "    \n",
    "def myLogLikelihood(mu, gamma, data):\n",
    "    if gamma > 0: # avoid taking log(0)\n",
    "        return np.sum(np.log(gamma) - np.log(gamma**2 + (data-mu)**2))\n",
    "    else:\n",
    "        return -np.inf\n",
    "\n",
    "# emcee expects the first argument to be a vector of parameters\n",
    "def myLogPosterior(parameters, data):\n",
    "    mu, gamma = parameters # unpack the parameters\n",
    "    return myLogPrior(mu, gamma) + myLogLikelihood(mu, gamma, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's set up and run the `emcee` sampler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "# emcee combines multiple \"walkers\", each of which is its own MCMC chain. \n",
    "# the number of trace results will be nwalkers * nsteps\n",
    "ndim = 2  # number of parameters in the model\n",
    "nwalkers = 6  # number of MCMC walkers\n",
    "Nburn = 1000  # \"burn-in\" period to let chains stabilize\n",
    "nsteps = 5000  # number of MCMC steps to take\n",
    "\n",
    "# initialize theta \n",
    "np.random.seed(0)\n",
    "starting_guesses = np.random.random((nwalkers, ndim))\n",
    "\n",
    "# the function call where all the work happens: \n",
    "sampler = emcee.EnsembleSampler(nwalkers, ndim, myLogPosterior, args=[xi])\n",
    "sampler.run_mcmc(starting_guesses, nsteps)\n",
    " \n",
    "# sampler.chain is of shape (nwalkers, nsteps, ndim)\n",
    "# throw-out the burn-in points\n",
    "# and merge all of the walkers into one chain\n",
    "emcee_trace  = sampler.chain[:, Nburn:, :].reshape(-1, ndim)\n",
    "\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The return is the sample from the distributon (known as a \"chain\", in MCMC):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.44451829, 1.12423926],\n",
       "       [0.64029353, 1.16299692],\n",
       "       [0.64029353, 1.16299692],\n",
       "       ...,\n",
       "       [0.55419371, 0.7931099 ],\n",
       "       [0.68504805, 0.58385836],\n",
       "       [0.66765879, 0.61166585]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emcee_trace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the full posterior by histogramming the sample, and compare it to the grid-based calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute histogram of results to plot below\n",
    "L_MCMC, mu_bins, gamma_bins = np.histogram2d(emcee_trace[:,0],\n",
    "                                             emcee_trace[:,1],\n",
    "                                             bins=(np.linspace(-5, 5, 41),\n",
    "                                                   np.linspace(0, 5, 41)))\n",
    "L_MCMC[L_MCMC == 0] = 1E-16  # prevents zero-division errors\n",
    "\n",
    "hist_mu, bins_mu = np.histogram(emcee_trace[:,0], bins=mu_bins, density=True)\n",
    "hist_gamma, bins_gamma = np.histogram(emcee_trace[:,1],\n",
    "                                      bins=gamma_bins, density=True)\n",
    "\n",
    "## this is code for the textbook figure 5.22, see\n",
    "## http://www.astroml.org/book_figures/chapter5/fig_cauchy_mcmc.html\n",
    "\n",
    "def cauchy_logL(xi, sigma, mu):\n",
    "    \"\"\"Equation 5.74: cauchy likelihood\"\"\"\n",
    "    xi = np.asarray(xi)\n",
    "    n = xi.size\n",
    "    shape = np.broadcast(sigma, mu).shape\n",
    "\n",
    "    xi = xi.reshape(xi.shape + tuple([1 for s in shape]))\n",
    "\n",
    "    return ((n - 1) * np.log(sigma)\n",
    "            - np.sum(np.log(sigma ** 2 + (xi - mu) ** 2), 0))\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------------------\n",
    "# Compute likelihood analytically for comparison\n",
    "mu = np.linspace(-5, 5, 70)\n",
    "gamma = np.linspace(0.1, 5, 70)\n",
    "logL = cauchy_logL(xi, gamma[:, np.newaxis], mu)\n",
    "logL -= logL.max()\n",
    "\n",
    "p_mu = np.exp(logL).sum(0)\n",
    "p_mu /= p_mu.sum() * (mu[1] - mu[0])\n",
    "\n",
    "p_gamma = np.exp(logL).sum(1)\n",
    "p_gamma /= p_gamma.sum() * (gamma[1] - gamma[0])\n",
    "\n",
    "hist_mu, bins_mu = np.histogram(emcee_trace[:,0], bins=mu_bins, density=True)\n",
    "hist_gamma, bins_gamma = np.histogram(emcee_trace[:, 1],\n",
    "                                      bins=gamma_bins, density=True)\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------------------\n",
    "# plot the results\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "# first axis: likelihood contours\n",
    "ax1 = fig.add_axes((0.4, 0.4, 0.55, 0.55))\n",
    "ax1.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax1.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "ax1.contour(mu, gamma, convert_to_stdev(logL),\n",
    "            levels=(0.683, 0.955, 0.997),\n",
    "            colors='b', linestyles='dashed')\n",
    "\n",
    "ax1.contour(0.5 * (mu_bins[:-1] + mu_bins[1:]),\n",
    "            0.5 * (gamma_bins[:-1] + gamma_bins[1:]),\n",
    "            convert_to_stdev(np.log(L_MCMC.T)),\n",
    "            levels=(0.683, 0.955, 0.997),\n",
    "            colors='k')\n",
    "\n",
    "# second axis: marginalized over mu\n",
    "ax2 = fig.add_axes((0.1, 0.4, 0.29, 0.55))\n",
    "ax2.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax2.plot(hist_gamma, 0.5 * (bins_gamma[1:] + bins_gamma[:-1]\n",
    "                            - bins_gamma[1] + bins_gamma[0]),\n",
    "         '-k', drawstyle='steps')\n",
    "ax2.plot(p_gamma, gamma, '--b')\n",
    "ax2.set_ylabel(r'$\\gamma$')\n",
    "ax2.set_ylim(0, 5)\n",
    "\n",
    "# third axis: marginalized over gamma\n",
    "ax3 = fig.add_axes((0.4, 0.1, 0.55, 0.29))\n",
    "ax3.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax3.plot(0.5 * (bins_mu[1:] + bins_mu[:-1]), hist_mu,\n",
    "         '-k', drawstyle='steps-mid')\n",
    "ax3.plot(mu, p_mu, '--b')\n",
    "ax3.set_xlabel(r'$\\mu$')\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The contours denote 1,2, and 3-sigma credible regions, corresponding to $68.3\\%$,  $95.5\\%$ and $99.7\\%$ probabilities.\n",
    "\n",
    "I.e., we claim that our state of knowledge is such that we believe the true values of $\\mu, \\gamma$ are enclosed by the 3rd contour with $99.7\\%$ confidence (and analogous for the inner-most and middle contour).\n",
    "\n",
    "Note how the knowledge of the outer-most contour is \"ratty\" -- increasing the number of samples will increase its fidelity. Whether you have to do that depends on how important is it for you to know which values are very unlikely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##  Markov Chain Monte Carlo with PyMC3\n",
    "\n",
    "We will next use pyMC3 on the same problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's sample the posterior with PyMC3.\n",
    "\n",
    "Unlike `emcee`, which takes an arbitrary Python function proportional to the log of the posterior, PyMC3 requires us to construct the posterior by composing its [built-in distributions](https://docs.pymc.io/api/distributions/continuous.html).\n",
    "\n",
    "We start by writing out the Bayes theorem:\n",
    "\n",
    "$$\n",
    "p(\\mu, \\gamma|D) \\, d\\mu \\, d\\gamma \\propto p(D|\\mu, \\gamma) \\cdot p(\\mu, \\gamma) \\, d\\mu \\, d\\gamma = Cauchy(D|\\mu, \\gamma) \\cdot U(\\mu) \\cdot J(\\gamma) \\, d\\mu \\, d\\gamma \n",
    "$$\n",
    "\n",
    "where $U$ is the uniform distribution, and $J$ is the uninformative prior $1/\\gamma$.\n",
    "\n",
    "It's computationally more efficient to use $\\log \\gamma$ as a variable, so we do the change of variables:\n",
    "$$ d\\log \\gamma = \\frac{d\\gamma}{\\gamma} \\\\ \\Rightarrow J(\\gamma) d\\gamma = \\frac{d\\gamma}{\\gamma} = d\\log \\gamma$$\n",
    "In the new variables, our posterior becomes:\n",
    "\n",
    "$$\n",
    "p(\\mu, \\log \\gamma|D) \\, d\\mu \\, d(\\log \\gamma) \\propto Cauchy(D|\\mu, e^{\\log \\gamma}) \\cdot U(\\mu) \\cdot U(\\log \\gamma) \\, d\\mu \\, d(\\log \\gamma)\n",
    "$$\n",
    "\n",
    "We can now go ahead and construct it with PyMC3 objects. \n",
    "\n",
    "Rather than explicitly multiplying the factors above, note how PyMC3 infers their relationships by via variable names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Sequential sampling (2 chains in 1 job)\n",
      "NUTS: [log_gamma, mu]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='13000' class='' max='13000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [13000/13000 00:10&lt;00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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       "        background: #F44336;\n",
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       "</style>\n"
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       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='13000' class='' max='13000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [13000/13000 00:10&lt;00:00 Sampling chain 1, 0 divergences]\n",
       "    </div>\n",
       "    "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 12_000 draw iterations (2_000 + 24_000 draws total) took 22 seconds.\n"
     ]
    }
   ],
   "source": [
    "## this is SIMPLIFIED code for the textbook figure 5.22, see\n",
    "## http://www.astroml.org/book_figures/chapter5/fig_cauchy_mcmc.html\n",
    "\n",
    "# set up our model for mu and log_gamma\n",
    "with pm.Model():\n",
    "    mu = pm.Uniform('mu', -5, 5)                  # the two numbers are the right/left limits of the uniform dist.\n",
    "    log_gamma = pm.Uniform('log_gamma', -10, 10)  # the two numbers are the right/left limits of the uniform dist.\n",
    "\n",
    "    # set up our likelihood. by setting observed=xi, we tell PyMC3 this is our likelihood.\n",
    "    x = pm.Cauchy('x', alpha=mu, beta=np.exp(log_gamma), observed=xi)\n",
    "\n",
    "    # this will start the sampler\n",
    "    trace = pm.sample(draws=12000, tune=1000, cores=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyMC3 used its default [No-U Turn Sampler (NUTS)](http://www.stat.columbia.edu/~gelman/research/published/nuts.pdf) sampler. It's very safe, but for some problems can be slow (we'll switch to Metropolis for that reason further below). (Check out Chi Feng's [animation of NUTS](https://chi-feng.github.io/mcmc-demo/app.html?algorithm=NaiveNUTS&target=banana).)\n",
    "\n",
    "Like with `emcee`, the output is a trace (a chain):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.87178399,  0.27727193,  0.84774421, ..., -0.23460382,\n",
       "       -0.45396154,  0.41643399])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace['mu']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.29700436,  0.74314042,  0.20022947, ..., -0.30863559,\n",
       "        0.15311075,  0.14886689])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace['log_gamma']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24000,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace['log_gamma'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot it (two-dimensional posterior + marginal distributions):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute histogram of results to plot below\n",
    "L_MCMC, mu_bins, gamma_bins = np.histogram2d(trace['mu'],\n",
    "                                             np.exp(trace['log_gamma']),\n",
    "                                             bins=(np.linspace(-5, 5, 41),\n",
    "                                                   np.linspace(0, 5, 41)))\n",
    "L_MCMC[L_MCMC == 0] = 1E-16  # prevents zero-division errors\n",
    "\n",
    "hist_mu, bins_mu = np.histogram(trace['mu'], bins=mu_bins, density=True)\n",
    "hist_gamma, bins_gamma = np.histogram(np.exp(trace['log_gamma']),\n",
    "                                      bins=gamma_bins, density=True)\n",
    "## this is code for the textbook figure 5.22, see\n",
    "## http://www.astroml.org/book_figures/chapter5/fig_cauchy_mcmc.html\n",
    "\n",
    "def cauchy_logL(xi, sigma, mu):\n",
    "    \"\"\"Equation 5.74: cauchy likelihood\"\"\"\n",
    "    xi = np.asarray(xi)\n",
    "    n = xi.size\n",
    "    shape = np.broadcast(sigma, mu).shape\n",
    "\n",
    "    xi = xi.reshape(xi.shape + tuple([1 for s in shape]))\n",
    "\n",
    "    return ((n - 1) * np.log(sigma)\n",
    "            - np.sum(np.log(sigma ** 2 + (xi - mu) ** 2), 0))\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------------------\n",
    "# Compute likelihood analytically for comparison\n",
    "mu = np.linspace(-5, 5, 70)\n",
    "gamma = np.linspace(0.1, 5, 70)\n",
    "logL = cauchy_logL(xi, gamma[:, np.newaxis], mu)\n",
    "logL -= logL.max()\n",
    "\n",
    "p_mu = np.exp(logL).sum(0)\n",
    "p_mu /= p_mu.sum() * (mu[1] - mu[0])\n",
    "\n",
    "p_gamma = np.exp(logL).sum(1)\n",
    "p_gamma /= p_gamma.sum() * (gamma[1] - gamma[0])\n",
    "\n",
    "hist_mu, bins_mu = np.histogram(trace['mu'], bins=mu_bins, density=True)\n",
    "hist_gamma, bins_gamma = np.histogram(np.exp(trace['log_gamma']),\n",
    "                                      bins=gamma_bins, density=True)\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------------------\n",
    "# plot the results\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "# first axis: likelihood contours\n",
    "ax1 = fig.add_axes((0.4, 0.4, 0.55, 0.55))\n",
    "ax1.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax1.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "\n",
    "ax1.contour(mu, gamma, convert_to_stdev(logL),\n",
    "            levels=(0.683, 0.955, 0.997),\n",
    "            colors='b', linestyles='dashed')\n",
    "\n",
    "ax1.contour(0.5 * (mu_bins[:-1] + mu_bins[1:]),\n",
    "            0.5 * (gamma_bins[:-1] + gamma_bins[1:]),\n",
    "            convert_to_stdev(np.log(L_MCMC.T)),\n",
    "            levels=(0.683, 0.955, 0.997),\n",
    "            colors='k')\n",
    "\n",
    "# second axis: marginalized over mu\n",
    "ax2 = fig.add_axes((0.1, 0.4, 0.29, 0.55))\n",
    "ax2.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax2.plot(hist_gamma, 0.5 * (bins_gamma[1:] + bins_gamma[:-1]\n",
    "                            - bins_gamma[1] + bins_gamma[0]),\n",
    "         '-k', drawstyle='steps')\n",
    "ax2.plot(p_gamma, gamma, '--b')\n",
    "ax2.set_ylabel(r'$\\gamma$')\n",
    "ax2.set_ylim(0, 5)\n",
    "\n",
    "# third axis: marginalized over gamma\n",
    "ax3 = fig.add_axes((0.4, 0.1, 0.55, 0.29))\n",
    "ax3.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax3.plot(0.5 * (bins_mu[1:] + bins_mu[:-1]), hist_mu,\n",
    "         '-k', drawstyle='steps-mid')\n",
    "ax3.plot(mu, p_mu, '--b')\n",
    "ax3.set_xlabel(r'$\\mu$')\n",
    "plt.xlim(-5, 5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Modelling bursts\n",
    "\n",
    "Imagine we observe an object that instantaneously brightens, then shows an exponential fall-off (e.g., a _very_ simplified supernova model):\n",
    "\n",
    "$$ F(t) = b_0 + A \\cdot e^{-\\alpha (t - T)} $$\n",
    "\n",
    "for $t > T$ and $F(t) = b_0$ before the burst. The parameters are the brightness before the burst $b_0$, amplitude of the burst, $A$, the time of the burst $T$, and the exponential fall-off constant $\\alpha$.\n",
    "\n",
    "We collect 100 observations with known photometric (Gaussian) errors. We wish to estimate the posterior of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A utility function to convert the traces (chains) to a Pandas DataFrame\n",
    "def traces_to_pandas(traces, burnin):\n",
    "    varnames = [ var for var in traces.varnames if not var.endswith(\"_interval__\") ]\n",
    "    cols = { var: traces[var, :burnin] for var in varnames }\n",
    "    return pd.DataFrame(cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate a mock the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ----------------------------------------------------------------------\n",
    "# Set up toy dataset\n",
    "def burst(t, b0, A, alpha, T):\n",
    "    \"\"\"Burst model\"\"\"\n",
    "    mask = (t >= T)\n",
    "    y = b0 + mask * A * np.exp(-alpha * (t - T))\n",
    "    return y\n",
    "\n",
    "np.random.seed(10)\n",
    "\n",
    "N = 100\n",
    "b0_true = 10\n",
    "A_true = 5\n",
    "alpha_true = 0.1\n",
    "T_true = 50\n",
    "sigma = 1.0\n",
    "\n",
    "t = np.sort(100 * np.random.random(N))\n",
    "\n",
    "y_true = burst(t, b0_true, A_true, alpha_true, T_true)\n",
    "y_obs = np.random.normal(y_true, sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 7))\n",
    "plt.scatter(t, y_obs)\n",
    "\n",
    "# uncomment to overplot the burst w/o observational errors.\n",
    "tt = np.linspace(0, 100, 1000)\n",
    "plt.gca().plot(tt, burst(tt, b0_true, A_true, alpha_true, T_true), c='red', linestyle=':');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up and sample a PyMC3 model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sequential sampling (1 chains in 1 job)\n",
      "CompoundStep\n",
      ">Metropolis: [log_alpha]\n",
      ">Metropolis: [T]\n",
      ">Metropolis: [A]\n",
      ">Metropolis: [b0]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='26000' class='' max='26000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [26000/26000 00:21&lt;00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 1 chain for 1_000 tune and 25_000 draw iterations (1_000 + 25_000 draws total) took 22 seconds.\n",
      "Only one chain was sampled, this makes it impossible to run some convergence checks\n"
     ]
    }
   ],
   "source": [
    "with pm.Model():\n",
    "    b0 = pm.Uniform('b0', 0, 50)\n",
    "    A = pm.Uniform('A', 0, 50)\n",
    "    T = pm.Uniform('T', 0, 100)\n",
    "    log_alpha = pm.Uniform('log_alpha', -10, 10)\n",
    "\n",
    "    y = pm.Normal('y', mu=burst(t, b0, A, np.exp(log_alpha), T), sd=sigma, observed=y_obs)\n",
    "\n",
    "    step = pm.Metropolis()\n",
    "    traces = pm.sample(draws=25000, tune=1000, step=step, chains=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = traces_to_pandas(traces, 1000)\n",
    "df[\"alpha\"] = np.exp(traces['log_alpha'])\n",
    "del df[\"log_alpha\"]\n",
    "\n",
    "variables = ['b0', 'A', 'T', 'alpha']\n",
    "labels = ['$b_0$', '$A$', '$T$', r'$\\alpha$']\n",
    "limits = [(9.2, 11.2), (2, 12), (45, 55), (0.0, 0.25)]\n",
    "true = [b0_true, A_true, T_true, alpha_true]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "fig.subplots_adjust(bottom=0.1, top=0.95,\n",
    "                    left=0.1, right=0.95,\n",
    "                    hspace=0.05, wspace=0.05)\n",
    "\n",
    "# This function plots multiple panels with the traces\n",
    "plot_mcmc([df[col] for col in variables],\n",
    "          labels=labels, limits=limits,\n",
    "          true_values=true, fig=fig, bins=30, colors='k')\n",
    "\n",
    "# Plot the model fit\n",
    "ax = fig.add_axes([0.5, 0.7, 0.45, 0.25])\n",
    "t_fit = np.linspace(0, 100, 1000)\n",
    "y_fit = burst(t_fit, **df.median())\n",
    "y_true = burst(t_fit, b0_true, A_true, alpha_true, T_true)\n",
    "\n",
    "ax.scatter(t, y_obs, s=9, lw=0, c='k')\n",
    "ax.plot(t_fit, y_true, '--', c='blue', label=\"true\")\n",
    "ax.plot(t_fit, y_fit, '-k', label=\"fit\")\n",
    "ax.legend()\n",
    "ax.set_xlim(0, 100)\n",
    "ax.set_xlabel('$t$')\n",
    "ax.set_ylabel(r'$h_{\\rm obs}$')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some things to notice:\n",
    "  * The fit appears reasonable. There's no indication there were issues with sampling.\n",
    "  * The contours enclose $68\\%$ and $95.5\\%$ probabilities\n",
    "  * Much of what's in the plot has a physical interpretation:\n",
    "    * Note how $T$ is tightly constrained  between $\\sim 49.9$ and $\\sim 51.2$, but poorly constrained inside of that interval? That is because the two data points bracketing the burst are at those timestamps -- they give a strong indication the burst couldn't have happened earlier or later, but constrain poorly when it happened inbetween.\n",
    "    * Note how $T$ and $A$ are strongly correlated? $T$ is poorly constrained between the two timesamps as discussed above. But if $T$ happened earlier (e.g., closer to $t = 49.9$), it that means the burst amplitude had to be *higher*, to explain the observed amplitude at $t=51.2$. Similarly, if it occurred closer to $51.2$, the amplitude had to be lower. That's what the tilt of the contours tells us.\n",
    "    * Similarly, note how $A$-$\\alpha$ are correlated. If the burst started with a larger amplitude, it had to fall off faster to fit the datapoints at large $t$ well. Conversely, if it started with a lower amplitude, it musn't fall off very fast otherwise it will underestimate the values at large $t$.\n",
    "    * Similar reasoning explains the correlation of $\\alpha$ and $b_0$.\n",
    "    * Finally, note the lack of correlation of $A$ and $b_0$. Increasing the amplitude doesn't have much of an effect on the estimate of $b_0$, as $b_0$ is primarily determined by data from before the burst happened.\n",
    "    \n",
    "**Studying correlations in posterior plots can bring insight into the physics, and develop our intuition about the relationships between model parameters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49.85070123025904, 51.21922633857766)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the observations bracketing the burst time, for the discussion above.\n",
    "t[t <= T_true][-1], t[t > T_true][0]"
   ]
  }
 ],
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