all normed keywords changed to density

Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb

@@ -803,7 +803,7 @@
     "ax.set_autoscaley_on(False)\n",
     "\n",
     "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
-    "         label=\"posterior of $\\lambda_1$\", color=\"#A60628\", normed=True)\n",
+    "         label=\"posterior of $\\lambda_1$\", color=\"#A60628\", density=True)\n",
     "plt.legend(loc=\"upper left\")\n",
     "plt.title(r\"\"\"Posterior distributions of the variables\n",
     "    $\\lambda_1,\\;\\lambda_2,\\;\\tau$\"\"\")\n",
@@ -813,7 +813,7 @@
     "ax = plt.subplot(312)\n",
     "ax.set_autoscaley_on(False)\n",
     "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
-    "         label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", normed=True)\n",
+    "         label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", density=True)\n",
     "plt.legend(loc=\"upper left\")\n",
     "plt.xlim([15, 30])\n",
     "plt.xlabel(\"$\\lambda_2$ value\")\n",

Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb

@@ -360,7 +360,7 @@
     "\n",
     "\n",
     "samples = [lambda_1.random() for i in range(20000)]\n",
-    "plt.hist(samples, bins=70, normed=True, histtype=\"stepfilled\")\n",
+    "plt.hist(samples, bins=70, density=True, histtype=\"stepfilled\")\n",
     "plt.title(\"Prior distribution for $\\lambda_1$\")\n",
     "plt.xlim(0, 8);"
    ]
@@ -826,7 +826,7 @@
     "figsize(12.5, 4)\n",
     "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n",
     "plt.vlines(p_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n",
-    "plt.hist(mcmc.trace(\"p\")[:], bins=25, histtype=\"stepfilled\", normed=True)\n",
+    "plt.hist(mcmc.trace(\"p\")[:], bins=25, histtype=\"stepfilled\", density=True)\n",
     "plt.legend();"
    ]
   },
@@ -979,7 +979,7 @@
     "\n",
     "plt.xlim(0, .1)\n",
     "plt.hist(p_A_samples, histtype='stepfilled', bins=25, alpha=0.85,\n",
-    "         label=\"posterior of $p_A$\", color=\"#A60628\", normed=True)\n",
+    "         label=\"posterior of $p_A$\", color=\"#A60628\", density=True)\n",
     "plt.vlines(true_p_A, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n",
     "plt.legend(loc=\"upper right\")\n",
     "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n",
@@ -988,13 +988,13 @@
     "\n",
     "plt.xlim(0, .1)\n",
     "plt.hist(p_B_samples, histtype='stepfilled', bins=25, alpha=0.85,\n",
-    "         label=\"posterior of $p_B$\", color=\"#467821\", normed=True)\n",
+    "         label=\"posterior of $p_B$\", color=\"#467821\", density=True)\n",
     "plt.vlines(true_p_B, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n",
     "plt.legend(loc=\"upper right\")\n",
     "\n",
     "ax = plt.subplot(313)\n",
     "plt.hist(delta_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
-    "         label=\"posterior of delta\", color=\"#7A68A6\", normed=True)\n",
+    "         label=\"posterior of delta\", color=\"#7A68A6\", density=True)\n",
     "plt.vlines(true_p_A - true_p_B, 0, 60, linestyle=\"--\",\n",
     "           label=\"true delta (unknown)\")\n",
     "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n",
@@ -1353,7 +1353,7 @@
    "source": [
     "figsize(12.5, 3)\n",
     "p_trace = mcmc.trace(\"freq_cheating\")[:]\n",
-    "plt.hist(p_trace, histtype=\"stepfilled\", normed=True, alpha=0.85, bins=30,\n",
+    "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30,\n",
     "         label=\"posterior distribution\", color=\"#348ABD\")\n",
     "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.3)\n",
     "plt.xlim(0, 1)\n",
@@ -1479,7 +1479,7 @@
    "source": [
     "figsize(12.5, 3)\n",
     "p_trace = mcmc.trace(\"freq_cheating\")[:]\n",
-    "plt.hist(p_trace, histtype=\"stepfilled\", normed=True, alpha=0.85, bins=30,\n",
+    "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30,\n",
     "         label=\"posterior distribution\", color=\"#348ABD\")\n",
     "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.2)\n",
     "plt.xlim(0, 1)\n",
@@ -1910,12 +1910,12 @@
     "plt.subplot(211)\n",
     "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n",
     "plt.hist(beta_samples, histtype='stepfilled', bins=35, alpha=0.85,\n",
-    "         label=r\"posterior of $\\beta$\", color=\"#7A68A6\", normed=True)\n",
+    "         label=r\"posterior of $\\beta$\", color=\"#7A68A6\", density=True)\n",
     "plt.legend()\n",
     "\n",
     "plt.subplot(212)\n",
     "plt.hist(alpha_samples, histtype='stepfilled', bins=35, alpha=0.85,\n",
-    "         label=r\"posterior of $\\alpha$\", color=\"#A60628\", normed=True)\n",
+    "         label=r\"posterior of $\\alpha$\", color=\"#A60628\", density=True)\n",
     "plt.legend();"
    ]
   },
@@ -2074,7 +2074,7 @@
     "prob_31 = logistic(31, beta_samples, alpha_samples)\n",
     "\n",
     "plt.xlim(0.995, 1)\n",
-    "plt.hist(prob_31, bins=1000, normed=True, histtype='stepfilled')\n",
+    "plt.hist(prob_31, bins=1000, density=True, histtype='stepfilled')\n",
     "plt.title(\"Posterior distribution of probability of defect, given $t = 31$\")\n",
     "plt.xlabel(\"probability of defect occurring in O-ring\");"
    ]

Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb

@@ -442,7 +442,7 @@
     "\n",
     "\n",
     "samples = lambda_1.random(size=20000)\n",
-    "plt.hist(samples, bins=70, normed=True, histtype=\"stepfilled\")\n",
+    "plt.hist(samples, bins=70, density=True, histtype=\"stepfilled\")\n",
     "plt.title(\"Prior distribution for $\\lambda_1$\")\n",
     "plt.xlim(0, 8);"
    ]
@@ -894,7 +894,7 @@
     "figsize(12.5, 4)\n",
     "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n",
     "plt.vlines(p_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n",
-    "plt.hist(burned_trace[\"p\"], bins=25, histtype=\"stepfilled\", normed=True)\n",
+    "plt.hist(burned_trace[\"p\"], bins=25, histtype=\"stepfilled\", density=True)\n",
     "plt.legend();"
    ]
   },
@@ -1049,7 +1049,7 @@
     "\n",
     "plt.xlim(0, .1)\n",
     "plt.hist(p_A_samples, histtype='stepfilled', bins=25, alpha=0.85,\n",
-    "         label=\"posterior of $p_A$\", color=\"#A60628\", normed=True)\n",
+    "         label=\"posterior of $p_A$\", color=\"#A60628\", density=True)\n",
     "plt.vlines(true_p_A, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n",
     "plt.legend(loc=\"upper right\")\n",
     "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n",
@@ -1058,13 +1058,13 @@
     "\n",
     "plt.xlim(0, .1)\n",
     "plt.hist(p_B_samples, histtype='stepfilled', bins=25, alpha=0.85,\n",
-    "         label=\"posterior of $p_B$\", color=\"#467821\", normed=True)\n",
+    "         label=\"posterior of $p_B$\", color=\"#467821\", density=True)\n",
     "plt.vlines(true_p_B, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n",
     "plt.legend(loc=\"upper right\")\n",
     "\n",
     "ax = plt.subplot(313)\n",
     "plt.hist(delta_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
-    "         label=\"posterior of delta\", color=\"#7A68A6\", normed=True)\n",
+    "         label=\"posterior of delta\", color=\"#7A68A6\", density=True)\n",
     "plt.vlines(true_p_A - true_p_B, 0, 60, linestyle=\"--\",\n",
     "           label=\"true delta (unknown)\")\n",
     "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n",
@@ -1428,7 +1428,7 @@
    "source": [
     "figsize(12.5, 3)\n",
     "p_trace = burned_trace[\"freq_cheating\"][15000:]\n",
-    "plt.hist(p_trace, histtype=\"stepfilled\", normed=True, alpha=0.85, bins=30, \n",
+    "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n",
     "         label=\"posterior distribution\", color=\"#348ABD\")\n",
     "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.3)\n",
     "plt.xlim(0, 1)\n",
@@ -1559,7 +1559,7 @@
    "source": [
     "figsize(12.5, 3)\n",
     "p_trace = burned_trace[\"freq_cheating\"]\n",
-    "plt.hist(p_trace, histtype=\"stepfilled\", normed=True, alpha=0.85, bins=30, \n",
+    "plt.hist(p_trace, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n",
     "         label=\"posterior distribution\", color=\"#348ABD\")\n",
     "plt.vlines([.05, .35], [0, 0], [5, 5], alpha=0.2)\n",
     "plt.xlim(0, 1)\n",
@@ -1968,12 +1968,12 @@
     "plt.subplot(211)\n",
     "plt.title(r\"Posterior distributions of the variables $\\alpha, \\beta$\")\n",
     "plt.hist(beta_samples, histtype='stepfilled', bins=35, alpha=0.85,\n",
-    "         label=r\"posterior of $\\beta$\", color=\"#7A68A6\", normed=True)\n",
+    "         label=r\"posterior of $\\beta$\", color=\"#7A68A6\", density=True)\n",
     "plt.legend()\n",
     "\n",
     "plt.subplot(212)\n",
     "plt.hist(alpha_samples, histtype='stepfilled', bins=35, alpha=0.85,\n",
-    "         label=r\"posterior of $\\alpha$\", color=\"#A60628\", normed=True)\n",
+    "         label=r\"posterior of $\\alpha$\", color=\"#A60628\", density=True)\n",
     "plt.legend();"
    ]
   },
@@ -2132,7 +2132,7 @@
     "prob_31 = logistic(31, beta_samples, alpha_samples)\n",
     "\n",
     "plt.xlim(0.995, 1)\n",
-    "plt.hist(prob_31, bins=1000, normed=True, histtype='stepfilled')\n",
+    "plt.hist(prob_31, bins=1000, density=True, histtype='stepfilled')\n",
     "plt.title(\"Posterior distribution of probability of defect, given $t = 31$\")\n",
     "plt.xlabel(\"probability of defect occurring in O-ring\");"
    ]

Chapter2_MorePyMC/Ch2_MorePyMC_TFP.ipynb

@@ -679,7 +679,7 @@
         "\n",
         "# Visualize our stepwise prior distribution\n",
         "plt.figure(figsize(12.5, 5))\n",
-        "plt.hist(lambda_1_, bins=70, normed=True, histtype=\"stepfilled\")\n",
+        "plt.hist(lambda_1_, bins=70, density=True, histtype=\"stepfilled\")\n",
         "plt.title(r\"Prior distribution for $\\lambda_1$\")\n",
         "plt.xlim(0, 8);"
       ],
@@ -1503,7 +1503,7 @@
         "plt.figure(figsize(12.5, 4))\n",
         "plt.title(\"Posterior distribution of $p_A$, the true effectiveness of site A\")\n",
         "plt.vlines(prob_true, 0, 90, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n",
-        "plt.hist(burned_prob_A_trace_, bins=25, histtype=\"stepfilled\", normed=True)\n",
+        "plt.hist(burned_prob_A_trace_, bins=25, histtype=\"stepfilled\", density=True)\n",
         "plt.legend();"
       ],
       "execution_count": 20,
@@ -1829,7 +1829,7 @@
         "\n",
         "plt.xlim(0, .1)\n",
         "plt.hist(burned_prob_A_trace_, histtype='stepfilled', bins=25, alpha=0.85,\n",
-        "         label=\"posterior of $p_A$\", color=TFColor[0], normed=True)\n",
+        "         label=\"posterior of $p_A$\", color=TFColor[0], density=True)\n",
         "plt.vlines(true_prob_A_, 0, 80, linestyle=\"--\", label=\"true $p_A$ (unknown)\")\n",
         "plt.legend(loc=\"upper right\")\n",
         "plt.title(\"Posterior distributions of $p_A$, $p_B$, and delta unknowns\")\n",
@@ -1838,13 +1838,13 @@
         "\n",
         "plt.xlim(0, .1)\n",
         "plt.hist(burned_prob_B_trace_, histtype='stepfilled', bins=25, alpha=0.85,\n",
-        "         label=\"posterior of $p_B$\", color=TFColor[2], normed=True)\n",
+        "         label=\"posterior of $p_B$\", color=TFColor[2], density=True)\n",
         "plt.vlines(true_prob_B_, 0, 80, linestyle=\"--\", label=\"true $p_B$ (unknown)\")\n",
         "plt.legend(loc=\"upper right\")\n",
         "\n",
         "ax = plt.subplot(313)\n",
         "plt.hist(burned_delta_trace_, histtype='stepfilled', bins=30, alpha=0.85,\n",
-        "         label=\"posterior of delta\", color=TFColor[6], normed=True)\n",
+        "         label=\"posterior of delta\", color=TFColor[6], density=True)\n",
         "plt.vlines(true_prob_A_ - true_prob_B_, 0, 60, linestyle=\"--\",\n",
         "           label=\"true delta (unknown)\")\n",
         "plt.vlines(0, 0, 60, color=\"black\", alpha=0.2)\n",
@@ -2796,7 +2796,7 @@
       "source": [
         "plt.figure(figsize(12.5, 6))\n",
         "p_trace_ = freq_cheating_samples_\n",
-        "plt.hist(p_trace_, histtype=\"stepfilled\", normed=True, alpha=0.85, bins=30, \n",
+        "plt.hist(p_trace_, histtype=\"stepfilled\", density=True, alpha=0.85, bins=30, \n",
         "         label=\"posterior distribution\", color=TFColor[3])\n",
         "plt.vlines([.1, .40], [0, 0], [5, 5], alpha=0.2)\n",
         "plt.xlim(0, 1)\n",

Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb

@@ -801,7 +801,7 @@
     "posterior_std_means = std_trace.mean(axis=0)\n",
     "posterior_p_mean = mcmc.trace(\"p\")[:].mean()\n",
     "\n",
-    "plt.hist(data, bins=20, histtype=\"step\", normed=True, color=\"k\",\n",
+    "plt.hist(data, bins=20, histtype=\"step\", density=True, color=\"k\",\n",
     "     lw=2, label=\"histogram of data\")\n",
     "y = posterior_p_mean * norm.pdf(x, loc=posterior_center_means[0],\n",
     "                                scale=posterior_std_means[0])\n",

Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb

@@ -785,7 +785,7 @@
     "posterior_std_means = std_trace.mean(axis=0)\n",
     "posterior_p_mean = trace[\"p\"].mean()\n",
     "\n",
-    "plt.hist(data, bins=20, histtype=\"step\", normed=True, color=\"k\",\n",
+    "plt.hist(data, bins=20, histtype=\"step\", density=True, color=\"k\",\n",
     "     lw=2, label=\"histogram of data\")\n",
     "y = posterior_p_mean * norm.pdf(x, loc=posterior_center_means[0],\n",
     "                                scale=posterior_std_means[0])\n",

Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb

@@ -601,10 +601,10 @@
     "for i in range(len(submissions)):\n",
     "    j = submissions[i]\n",
     "    posteriors.append(posterior_upvote_ratio(votes[j, 0], votes[j, 1]))\n",
-    "    plt.hist(posteriors[i], bins=18, normed=True, alpha=.9,\n",
+    "    plt.hist(posteriors[i], bins=18, density=True, alpha=.9,\n",
     "             histtype=\"step\", color=colours[i % 5], lw=3,\n",
     "             label='(%d up:%d down)\\n%s...' % (votes[j, 0], votes[j, 1], contents[j][:50]))\n",
-    "    plt.hist(posteriors[i], bins=18, normed=True, alpha=.2,\n",
+    "    plt.hist(posteriors[i], bins=18, density=True, alpha=.2,\n",
     "             histtype=\"stepfilled\", color=colours[i], lw=3, )\n",
     "\n",
     "plt.legend(loc=\"upper left\")\n",
@@ -654,10 +654,10 @@
     "\n",
     "for i in range(len(submissions)):\n",
     "    j = submissions[i]\n",
-    "    plt.hist(posteriors[i], bins=20, normed=True, alpha=.9,\n",
+    "    plt.hist(posteriors[i], bins=20, density=True, alpha=.9,\n",
     "             histtype=\"step\", color=colours[i], lw=3,\n",
     "             label='(%d up:%d down)\\n%s...' % (votes[j, 0], votes[j, 1], contents[j][:50]))\n",
-    "    plt.hist(posteriors[i], bins=20, normed=True, alpha=.2,\n",
+    "    plt.hist(posteriors[i], bins=20, density=True, alpha=.2,\n",
     "             histtype=\"stepfilled\", color=colours[i], lw=3, )\n",
     "    v = np.sort(posteriors[i])[int(0.05 * N)]\n",
     "    # plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n",

Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb

@@ -297,7 +297,7 @@
     "plt.plot(x, stats.norm.pdf(x, 35000, 7500), c=\"k\", lw=2,\n",
     "         label=\"prior dist. of suite price\")\n",
     "\n",
-    "_hist = plt.hist(price_trace, bins=35, normed=True, histtype=\"stepfilled\")\n",
+    "_hist = plt.hist(price_trace, bins=35, density=True, histtype=\"stepfilled\")\n",
     "plt.title(\"Posterior of the true price estimate\")\n",
     "plt.vlines(mu_prior, 0, 1.1 * np.max(_hist[0]), label=\"prior's mean\",\n",
     "           linestyles=\"--\")\n",

Chapter5_LossFunctions/Ch5_LossFunctions_TFP.ipynb

@@ -722,7 +722,7 @@
         "plt.plot(prices_, prior_, c=\"k\", lw=2,\n",
         "         label=\"prior dist. of suite price\")\n",
         "\n",
-        "hist = plt.hist(posterior_price_predictive_samples_, bins=35, normed=True, histtype=\"stepfilled\")\n",
+        "hist = plt.hist(posterior_price_predictive_samples_, bins=35, density=True, histtype=\"stepfilled\")\n",
         "plt.title(\"Posterior of the true price estimate\")\n",
         "plt.vlines(mu_prior, 0, 1.1 * np.max(hist[0]), label=\"prior's mean\",\n",
         "           linestyles=\"--\")\n",

Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb

@@ -1184,7 +1184,7 @@
     "    returns[:, i] = _returns\n",
     "    plt.subplot(2, 2, i+1)\n",
     "    plt.hist(_returns, bins=20,\n",
-    "             normed=True, histtype=\"stepfilled\",\n",
+    "             density=True, histtype=\"stepfilled\",\n",
     "             color=colors[i], alpha=0.7)\n",
     "    plt.title(_stock + \" returns\")\n",
     "    plt.xlim(-0.15, 0.15)\n",
@@ -1258,7 +1258,7 @@
     "\n",
     "for i in range(4):\n",
     "    plt.hist(mu_samples[:, i], alpha=0.8 - 0.05 * i, bins=30,\n",
-    "             histtype=\"stepfilled\", normed=True,\n",
+    "             histtype=\"stepfilled\", density=True,\n",
     "             label=\"%s\" % list(stock_returns.keys())[i])\n",
     "\n",
     "plt.vlines(mu_samples.mean(axis=0), 0, 500, linestyle=\"--\", linewidth=.5)\n",
@@ -1302,7 +1302,7 @@
     "for i in range(4):\n",
     "    plt.subplot(2, 2, i + 1)\n",
     "    plt.hist(mu_samples[:, i], alpha=0.8 - 0.05 * i, bins=30,\n",
-    "             histtype=\"stepfilled\", normed=True, color=colors[i],\n",
+    "             histtype=\"stepfilled\", density=True, color=colors[i],\n",
     "             label=\"%s\" % list(stock_returns.keys())[i])\n",
     "    plt.title(\"%s\" % list(stock_returns.keys())[i])\n",
     "    plt.xlim(-0.15, 0.15)\n",

Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb

@@ -1258,7 +1258,7 @@
     "\n",
     "for i in range(4):\n",
     "    plt.hist(mu_samples[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n",
-    "             histtype=\"stepfilled\", normed=True, \n",
+    "             histtype=\"stepfilled\", density=True, \n",
     "             label = \"%s\" % stock_returns.columns[i])\n",
     "\n",
     "plt.vlines(mu_samples.mean(axis=0), 0, 500, linestyle=\"--\", linewidth = .5)\n",
@@ -1302,7 +1302,7 @@
     "for i in range(4):\n",
     "    plt.subplot(2,2,i+1)\n",
     "    plt.hist(mu_samples[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n",
-    "             histtype=\"stepfilled\", normed=True, color = colors[i],\n",
+    "             histtype=\"stepfilled\", density=True, color = colors[i],\n",
     "             label = \"%s\" % stock_returns.columns[i])\n",
     "    plt.title(\"%s\" % stock_returns.columns[i])\n",
     "    plt.xlim(-0.15, 0.15)\n",

Chapter6_Priorities/Ch6_Priors_TFP.ipynb

@@ -2313,7 +2313,7 @@
         "\n",
         "for i in range(4):\n",
         "    plt.hist(mu_samples_[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n",
-        "             histtype=\"stepfilled\", color=colors[i], normed=True, \n",
+        "             histtype=\"stepfilled\", color=colors[i], density=True, \n",
         "             label = \"%s\" % stock_returns.columns[i])\n",
         "\n",
         "plt.vlines(mu_samples_.mean(axis=0), 0, 500, linestyle=\"--\", linewidth = .5)\n",
@@ -2373,7 +2373,7 @@
         "for i in range(4):\n",
         "    plt.subplot(2,2,i+1)\n",
         "    plt.hist(mu_samples_[:,i], alpha = 0.8 - 0.05*i, bins = 30,\n",
-        "             histtype=\"stepfilled\", normed=True, color = colors[i],\n",
+        "             histtype=\"stepfilled\", density=True, color = colors[i],\n",
         "             label = \"%s\" % stock_returns.columns[i])\n",
         "    plt.title(\"%s\" % stock_returns.columns[i])\n",
         "    plt.xlim(-0.15, 0.15)\n",

sandbox/CommitDataForChapter1.ipynb

@@ -160,7 +160,7 @@
       "ax.set_autoscaley_on(False)\n",
       "\n",
       "plt.hist(lambda_1_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
-      "         label=\"posterior of $\\lambda_1$\", color=\"#A60628\", normed=True)\n",
+      "         label=\"posterior of $\\lambda_1$\", color=\"#A60628\", density=True)\n",
       "plt.legend(loc=\"upper left\")\n",
       "plt.title(r\"Posterior distributions of the variables \\\n",
       "    $\\lambda_1,\\;\\lambda_2,\\;\\tau$\")\n",
@@ -171,7 +171,7 @@
       "ax.set_autoscaley_on(False)\n",
       "\n",
       "plt.hist(lambda_2_samples, histtype='stepfilled', bins=30, alpha=0.85,\n",
-      "         label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", normed=True)\n",
+      "         label=\"posterior of $\\lambda_2$\", color=\"#7A68A6\", density=True)\n",
       "plt.legend(loc=\"upper left\")\n",
       "\n",
       "plt.xlabel(\"$\\lambda_2$ value\")\n",