deletions | additions       diff --git a/Time Delay Estimation Metrics.tex b/Time Delay Estimation Metrics.tex  index 0411c26..2350318 100644  --- a/Time Delay Estimation Metrics.tex  +++ b/Time Delay Estimation Metrics.tex 
...
We can now roughly estimate the available precision on $H_0$ from the sample as $P \approx \langle P_k \rangle / \sqrt{N}$: if P is to reach 0.2\%, from a sample of 1000 lenses, we require an approximate average precision per lens of $\langle P_k \rangle \approx 6.3\%$. In turn, this implies that we need to be able to measure individual time delays to 3.8\% precision, or better, on average (in order to stay under 6.3\% when combined in quadrature with the 5\% mass model uncertainty).   Returning to the emulated analysis, we imagine evaluating the product of PDFs in Equation~\ref{eq:prodpdf}, and plotting the resulting likelihood for $H_0$. This distribution will have some median $\hat{H_0}$ and 68\% confidence interval $\sigma_{H_0}}$. $\sigma_{H_0}$.  From these values we define the precision (as already seen above) as \begin{equation}  P = \frac{\sigma_{H_0}}/H_0^* \times 100\%  \end{equation}