deletions | additions       diff --git a/Burst_Weights_Theory.tex b/Burst_Weights_Theory.tex  index 93b637e..ef37c33 100644  --- a/Burst_Weights_Theory.tex  +++ b/Burst_Weights_Theory.tex 
...
= \frac{1}{N} \frac{\sum_i n_{ti} \frac{n_{ai}}{n_{ti}} }{\sum_i n_{ti}} = \hat{E}  \end{equation}  Since $\hat{E}$ is the MVUB estimator, any other estimator of $E_p$ (in particular   the unweighted mean of $E_i$) will have a larger variance.  Note finally that, we can extend these consideration of optimal weights for   the population PR estimator to the plot of the actual distribution. Building an  unweighted histogram and fitting the peak is analogous to estimating the   $E_p$ with an unweighted average. Conversely, building the FRET histogram  using the burst size as weights and fitting the peak is equivalent to using  the MVUB estimator for $E_p$.