Tom's edits of two lectures, Feb 17

lectures/multivariate_normal.md

@@ -1454,6 +1454,13 @@ y_{T}
 \alpha_{0}\\
 \vdots\\
 \alpha_{0}
+\end{array}\right]}} +\underset{\equiv u}{\underbrace{\left[\begin{array}{c}
+u_{1} \\
+u_2 \\
+u_3\\
+u_4\\
+\vdots\\
+u_T
 \end{array}\right]}}
 $$
 

lectures/prob_meaning.md

@@ -710,15 +710,13 @@ Typically, the functional form of the likelihood function determines the functio
 
 A natural question to ask is why should a person's personal prior about a parameter $\theta$ be restricted to be described by a conjugate prior?
 
-Why not some other functional form that more sincerely describes the person's beliefs.
+Why not some other functional form that more sincerely describes the person's beliefs?
 
-To be argumentative, one could ask, why should the form of the likelihood function have *anything* to say about my
-personal beliefs about $\theta$?
+To be argumentative, one could ask, why should the form of the likelihood function have *anything* to say about my personal beliefs about $\theta$?
 
 A dignified response to that question is, well, it shouldn't, but if you want to compute a posterior easily you'll just be happier if your prior is conjugate to your likelihood.
 
-Otherwise, your posterior won't have a convenient analytical form and you'll be in the situation of wanting to
-apply the Markov chain Monte Carlo techniques deployed in {doc}`this quantecon lecture <bayes_nonconj>`.
+Otherwise, your posterior won't have a convenient analytical form and you'll be in the situation of wanting to apply the Markov chain Monte Carlo techniques deployed in {doc}`this quantecon lecture <bayes_nonconj>`.
 
 We also apply these powerful methods to approximating Bayesian posteriors for non-conjugate priors in
 {doc}`this quantecon lecture <ar1_bayes>` and {doc}`this quantecon lecture <ar1_turningpts>`