Update introduction.ipynb (#28)

occurence -> occurrence

docs/notebooks/introduction.ipynb

@@ -469,9 +469,9 @@
     "$$\n",
     "\\lambda (t|x_{i}) =\\lambda_{0}(t)\\theta(x_{i})\n",
     "$$\n",
-    "The baseline hazard $\\lambda_{0}(t)$ is identical across subjects (i.e., has no dependency on $i$). The subject-specific risk of event occurence is captured through the relative hazards $\\{\\theta(x_{i})\\}_{i = 1, \\dots, N}$.\n",
+    "The baseline hazard $\\lambda_{0}(t)$ is identical across subjects (i.e., has no dependency on $i$). The subject-specific risk of event occurrence is captured through the relative hazards $\\{\\theta(x_{i})\\}_{i = 1, \\dots, N}$.\n",
     "\n",
-    "We train a multi-layer perceptron (MLP) to model the subject-specific risk of event occurence, i.e., the log relative hazards $\\log\\theta(x_{i})$. Patients with lower recurrence time are assumed to have higher risk of event. "
+    "We train a multi-layer perceptron (MLP) to model the subject-specific risk of event occurrence, i.e., the log relative hazards $\\log\\theta(x_{i})$. Patients with lower recurrence time are assumed to have higher risk of event. "
    ]
   },
   {
@@ -768,7 +768,7 @@
     "\\lambda (t|x_{i}) = \\frac{\\rho(x_{i}) } {\\lambda(x_{i}) } + \\left(\\frac{t}{\\lambda(x_{i})}\\right)^{\\rho(x_{i}) - 1}\n",
     "$$\n",
     "\n",
-    "Given the hazard form, it can be shown that the event density follows a Weibull distribution parametrized by scale $\\lambda(x_{i})$ and shape $\\rho(x_{i})$. The subject-specific risk of event occurence at time $t$ is captured through the hazards $\\{\\lambda (t|x_{i})\\}_{i = 1, \\dots, N}$. We train a multi-layer perceptron (MLP) to model the subject-specific log scale, $\\log \\lambda(x_{i})$, and the log shape, $\\log\\rho(x_{i})$. "
+    "Given the hazard form, it can be shown that the event density follows a Weibull distribution parametrized by scale $\\lambda(x_{i})$ and shape $\\rho(x_{i})$. The subject-specific risk of event occurrence at time $t$ is captured through the hazards $\\{\\lambda (t|x_{i})\\}_{i = 1, \\dots, N}$. We train a multi-layer perceptron (MLP) to model the subject-specific log scale, $\\log \\lambda(x_{i})$, and the log shape, $\\log\\rho(x_{i})$. "
    ]
   },
   {