we draw a particular outcome of a random variable, which is a random variate

Author: Alec Hanefeld

Chapter3_MCMC/IntroMCMC.ipynb

@@ -318,7 +318,7 @@
       "This dataset is a good example of the data-generation modeling technique from last chapter. We can propose *how* the data might have been created. I suggest the following data generation algorithm: \n",
       "\n",
       "1. For each data point, choose cluster 1 with probability $p$, else choose cluster 2. \n",
-      "2. Draw a random variable from a Normal distribution with parameters $\\mu_i$ and $\\sigma_i$ where $i$ was chosen in step 1.\n",
+      "2. Draw a random variate from a Normal distribution with parameters $\\mu_i$ and $\\sigma_i$ where $i$ was chosen in step 1.\n",
       "3. Repeat.\n",
       "\n",
       "This algorithm would create a similar effect as the observed dataset, so we choose this as our model. Of course, we do not know $p$ or the parameters of the Normal distributions. Hence we must infer, or *learn*, these unknowns.\n",