Merge pull request #162 from williamscott/master

Correct typos

Author: CamDavidsonPilon

Chapter2_MorePyMC/MorePyMC.ipynb

@@ -152,7 +152,7 @@
       "\n",
       "where 0, 4 are the `DiscreteUniform`-specific lower and upper bound on the random variable. The [PyMC docs](http://pymc-devs.github.com/pymc/distributions.html) contain the specific parameters for stochastic variables. (Or use `??` if you are using IPython!)\n",
       "\n",
-      "The `name` attribute is used to retrieve the posterior distribution later in the analysis, so it is best to use a descriptive name. Typically, I use the Python variable's name as the name.\n",
+      "The `name` attribute is used to retrieve the posterior distribution later in the analysis, so it is best to use a descriptive name. Typically, I use the Python variable's name as the `name`.\n",
       "\n",
       "For multivariable problems, 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",
@@ -1787,7 +1787,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it is called a *bias*. )\n",
+      "Adding a constant term $\\alpha$ amounts to shifting the curve left or right (hence why it is called a *bias*).\n",
       "\n",
       "Let's start modeling this in PyMC. The $\\beta, \\alpha$ parameters have no reason to be positive, bounded or relatively large, so they are best modeled by a *Normal random variable*, introduced next."
      ]
@@ -1798,7 +1798,7 @@
      "source": [
       "### Normal distributions\n",
       "\n",
-      "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the *precision*, $\\tau$. Those familiar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau^{-1}$. They are in fact reciprocals of each other. The change was motivated by simpler mathematical analysis and is an artifact of older Bayesian methods. Just remember: The smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive. \n",
+      "A Normal random variable, denoted $X \\sim N(\\mu, 1/\\tau)$, has a distribution with two parameters: the mean, $\\mu$, and the *precision*, $\\tau$. Those familiar with the Normal distribution already have probably seen $\\sigma^2$ instead of $\\tau^{-1}$. They are in fact reciprocals of each other. The change was motivated by simpler mathematical analysis and is an artifact of older Bayesian methods. Just remember: the smaller $\\tau$, the larger the spread of the distribution (i.e. we are more uncertain); the larger $\\tau$, the tighter the distribution (i.e. we are more certain). Regardless, $\\tau$ is always positive. \n",
       "\n",
       "The probability density function of a $N( \\mu, 1/\\tau)$ random variable is:\n",
       "\n",
@@ -2757,4 +2757,4 @@
    "metadata": {}
   }
  ]
-}
+}