Correct typos

1.  Change reference to Python name attribute to use code formatting (to
distinguish it from ordinary language use of name).
2. Move fullstop from inside bracketed clause to outside.
3. Change upper case T after colon to lower case t.

Author: williamscott

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": {}
   }
  ]
-}
+}