Editorial tweaks, chap 2, pt 1

Minor spelling and grammar corrections, a few rewordings, etc...

Author: pmagwene

Chapter2_MorePyMC/MorePyMC.ipynb

@@ -89,13 +89,13 @@
       "\n",
       "### Parent and Child relationships\n",
       "\n",
-      "To assist with terminology, and to be consistent with PyMC's documentation, we introduce *parent and children* variables. \n",
+      "To assist with terminology, and to be consistent with PyMC's documentation, we introduce *parent and child* variables. \n",
       "\n",
       "*  *parent variables* are variables that influence another variable. \n",
       "\n",
-      "*  *children variable* are variables that are affected by other variables, i.e. are the subject of parent variables. \n",
+      "*  *child variable* are variables that are affected by other variables, i.e. are the subject of parent variables. \n",
       "\n",
-      "Variables can be both parent and children variables. For example, consider the PyMC code below"
+      "Variables can be both parents and children. For example, consider the PyMC code below"
      ]
     },
     {
@@ -178,7 +178,7 @@
      "source": [
       "### PyMC Variables\n",
       "\n",
-      "All PyMC variables also expose a `value` attribute. This method produces the *current* (possible random) value of the variable, given the variable's parents. To use the same variables from before:"
+      "All PyMC variables also expose a `value` attribute. This method produces the *current* (possibly random) value of the variable, given the variable's parents. To use the same variables from before:"
      ]
     },
     {
@@ -240,7 +240,7 @@
       "\n",
       "Rather than creating a Python array of stochastic variables, addressing the `size` keyword in the call to a `Stochastic` variable creates multivariate array of (independent) stochastic variables. The array behaves like a Numpy array when used like one, and references to its `value` attribute return Numpy arrays.  \n",
       "\n",
-      "The also solves the annoying case where you may have many variables $\\beta_i, \\; i = 1,...,N$ you wish to model. Instead of creating arbitrary names and variables for each one, like:\n",
+      "The `size` argument also solves the annoying case where you may have many variables $\\beta_i, \\; i = 1,...,N$ you wish to model. Instead of creating arbitrary names and variables for each one, like:\n",
       "\n",
       "    beta_1 = mc.Uniform( \"beta_1\", 0, 1)\n",
       "    beta_2 = mc.Uniform( \"beta_2\", 0, 1)\n",
@@ -349,7 +349,7 @@
      "source": [
       "#### Determinstic variables\n",
       "\n",
-      "Since most variables you will be modeling are stochastic, we distinguish deterministic variables with a `pymc.deterministic` wrapper. If you are unfamiliar with Python wrappers, that's no problem. Just preppend the `pymc.deterministic` and your good to go. No need to know know more. Preprending with the wrapper is the easist way, but not the only way, to create deterministic variables. This is not completely true: elementary operations, like addition, exponentials etc. implicity create determinsitic variables. "
+      "Since most variables you will be modeling are stochastic, we distinguish deterministic variables with a `pymc.deterministic` wrapper. If you are unfamiliar with Python wrappers (also called decorators), that's no problem. Just preppend the `pymc.deterministic` decorator and you're good to go. No need to know know more. Preprending with the wrapper is the easist way, but not the only way, to create deterministic variables. This is not completely true: elementary operations, like addition, exponentials etc. implicity create determinsitic variables. "
      ]
     },
     {