fix typos in notebooks

jakevdp · Jan 5, 2016 · 7c22fbc · 7c22fbc
1 parent e02b458
commit 7c22fbc
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Showing 5 changed files with 26 additions and 15 deletions.
diff --git a/02-Simple-Bayesian-Modeling.ipynb b/02-Simple-Bayesian-Modeling.ipynb
@@ -45,9 +45,9 @@
    "source": [
     "def make_data(intercept, slope, N=20, dy=5, rseed=42):\n",
     "    rand = np.random.RandomState(rseed)\n",
-    "    x = 100 * rand.rand(20)\n",
+    "    x = 100 * rand.rand(N)\n",
     "    y = intercept + slope * x\n",
-    "    y += dy * rand.randn(20)\n",
+    "    y += dy * rand.randn(N)\n",
     "    return x, y, dy * np.ones_like(x)\n",
     "\n",
     "theta_true = [25, 0.5]\n",
@@ -225,9 +225,9 @@
    },
    "outputs": [],
    "source": [
-    "x = np.linspace(-1, 1)\n",
+    "xx = np.linspace(-1, 1)\n",
     "for slope in np.linspace(0, 20, 100):\n",
-    "    plt.plot(x, slope * x, '-k', linewidth=1)\n",
+    "    plt.plot(xx, slope * xx, '-k', linewidth=1)\n",
     "plt.axis([-1, 1, -1, 1], aspect='equal');"
    ]
   },
@@ -329,7 +329,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": []

diff --git a/03-Bayesian-Modeling-With-MCMC.ipynb b/03-Bayesian-Modeling-With-MCMC.ipynb
@@ -104,7 +104,8 @@
     "\n",
     "There are several good Python approaches to Bayesian computation with MCMC. You can read about some of them in this blog post: [How to Be a Bayesian in Python](http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/).\n",
     "\n",
-    "Here we'll focus on [``emcee``](http://dan.iel.fm/emcee/), a lightweight Python package developed by Dan Foreman-Mackey and collaborators."
+    "Here we'll focus on [``emcee``](http://dan.iel.fm/emcee/), a lightweight Python package developed by Dan Foreman-Mackey and collaborators.\n",
+    "One benefit of ``emcee`` is that it uses an *ensemble sampler* which automatically tunes the shape and size of the proposal distribution (you can read more details in the ``emcee`` documentation)."
    ]
   },
   {
@@ -138,9 +139,9 @@
    "source": [
     "def make_data(intercept, slope, N=20, dy=5, rseed=42):\n",
     "    rand = np.random.RandomState(rseed)\n",
-    "    x = 100 * rand.rand(20)\n",
+    "    x = 100 * rand.rand(N)\n",
     "    y = intercept + slope * x\n",
-    "    y += dy * rand.randn(20)\n",
+    "    y += dy * rand.randn(N)\n",
     "    return x, y, dy * np.ones_like(x)\n",
     "\n",
     "theta_true = (25, 0.5)\n",
@@ -402,7 +403,7 @@
     "    # fill this in\n",
     "    pass\n",
     "\n",
-    "def log_posterior(theta, x, y, sigma):\n",
+    "def log_posterior(theta, x, y, dy):\n",
     "    # fill this in\n",
     "    pass"
    ]

diff --git a/05-Radial-Velocity.ipynb b/05-Radial-Velocity.ipynb
@@ -221,7 +221,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It seems like we have a reasonable model!"
+    "The model seems to be working as expected!"
    ]
   },
   {
@@ -392,7 +392,7 @@
     "from gatspy.periodic import LombScargleFast\n",
     "model = LombScargleFast()\n",
     "\n",
-    "model.fit(t, rv, rv_err)\n",
+    "model.fit(t_sim, rv_sim, err_sim)\n",
     "periods, power = model.periodogram_auto()\n",
     "plt.semilogx(periods, power)\n",
     "plt.xlim(0, 10000);"

diff --git a/Solutions-02.ipynb b/Solutions-02.ipynb
@@ -259,7 +259,17 @@
    "outputs": [],
    "source": [
     "x2, y2, dy2 = make_data(*theta_true, N=3, dy=40)\n",
-    "plt.errorbar(x2, y2, dy2, fmt='o');\n",
+    "plt.errorbar(x2, y2, dy2, fmt='o');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
     "plot_results(x2, y2, dy2,\n",
     "             slope_limits=(-2, 5),\n",
     "             intercept_limits=(-300, 200))"

diff --git a/Solutions-03.ipynb b/Solutions-03.ipynb
@@ -83,16 +83,16 @@
     "        return -np.inf  # log(0)\n",
     "    else:\n",
     "        # Jeffreys Prior\n",
-    "        return -theta[2]\n",
+    "        return -np.log(theta[2])\n",
     "    \n",
     "def log_likelihood(theta, x, y, dy):\n",
     "    y_model = theta[0] + theta[1] * x\n",
     "    S = dy ** 2 + theta[2] ** 2\n",
     "    return -0.5 * np.sum(np.log(2 * np.pi * S) +\n",
     "                         (y - y_model) ** 2 / S)\n",
     "\n",
-    "def log_posterior(theta, x, y, sigma):\n",
-    "    return log_prior(theta) + log_likelihood(theta, x, y, sigma)"
+    "def log_posterior(theta, x, y, dy):\n",
+    "    return log_prior(theta) + log_likelihood(theta, x, y, dy)"
    ]
   },
   {