deletions | additions       diff --git a/Burst_Variance_Analysis.tex b/Burst_Variance_Analysis.tex  index 02ae7fb..3cbe43e 100644  --- a/Burst_Variance_Analysis.tex  +++ b/Burst_Variance_Analysis.tex 
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FRETBurts provides well-tested, general-purpose functions for timestamps and burst data   manipulation and therefore simplifies implementing custom burst analysis algorithms such as BVA.  \subsection{An Introduction to BVA} Burst Variance Analysis (BVA)}  Single-molecule FRET histograms show more information than just mean FRET efficiencies.   While, in general, several peaks indicate the presence of multiple subpopulations,   a single peak cannot be a priori associated with a single FRET efficiency, 
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the sub-bursts acceptor counts ($N_a$) will ideally follow a binomial distribution of   eq.~\ref{eq:binom_dist}, where $n$ is the number of photons in each sub-burst and   $E$ is the FRET efficiency. In practice, we use the proximity ratio peak position as $E$,  and we expect the standard deviation of  $E$ for the sub-bursts to be distributed according to eq.~\ref{eq:binom_std}. This is an approximation because background counts (both from  sample and detector's dark counts) add additional variance that is not taken into account.  However, the approximation works well in practical cases because the background contribution