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authorPaul <Paul@Pauls-MacBook-Air.local>2014-09-26 01:00:31 -0400
committerPaul <Paul@Pauls-MacBook-Air.local>2014-09-26 01:00:31 -0400
commitcc02745c759c77d5c1a13927338ebd0cd0a0bf35 (patch)
tree90bfaa8aabfd7e19ceed5705accd5fac7cce3854 /ps1
parent411a67dc526eaf5580dd231c3d3b3dc60d080b63 (diff)
downloadecon2099-cc02745c759c77d5c1a13927338ebd0cd0a0bf35.tar.gz
Fixing typo
Diffstat (limited to 'ps1')
-rw-r--r--ps1/main.tex4
1 files changed, 2 insertions, 2 deletions
diff --git a/ps1/main.tex b/ps1/main.tex
index 35ad24b..ac1a2b9 100644
--- a/ps1/main.tex
+++ b/ps1/main.tex
@@ -285,7 +285,7 @@ a simpler form.
\end{cases}
\end{displaymath}
then our optimization problem becomes a fractional Knapsack problem for
- which the optimal solution is easy to compute: order agents by
+ which the optimal solution is easy to compute: order agents by
decreasing order of expected values and allocate to them in this order
until the budget is exhausted.
\end{enumerate}
@@ -339,7 +339,7 @@ Since $\sum_i v_i$ is a symmetric random variable centered at $\frac{n}{2}$.
Now we have to compute $\E\left[I_n\sum_{i=1}^n v_i\right]$. It is hard to
obtain a closed-form formula, but we can obtain an asymptotic approximation
-when $n$ goes to infinity by observing that the sum of iid variables behaves
+when $n$ goes to infinity by observing that the sum of i.i.d. variables behaves
like a normal distribution in the limit. This can be made formal by applying
the central limit theorem. Let us define:
\begin{equation}\label{eq:xn}