deletions | additions       diff --git a/Burst_Variance_Analysis.tex b/Burst_Variance_Analysis.tex  index 8d0f6e4..b588286 100644  --- a/Burst_Variance_Analysis.tex  +++ b/Burst_Variance_Analysis.tex 
...
\section{Implementing Burst Variance Analysis}  In this section we describe how to implement the Burst Variance Analysis (BVA)~\cite{Torella_2011},  as an example of building custom burst analysis algorithms in FRETBursts.  Single-molecule  FRET histogramscould  show more information than just mean FRET efficiencies. Broad While, in general, several peaks indicate the presence of multiple subpopulations,   a single peak cannot be associated a priori with a single FRET efficiency,  unless a detailed shot-noise analysis is carried out~\cite{Nir_2006} ADD SEIDEL PDA PAPER.  A broad  FRET distributions distribution  might be attributed to the a  mixture of multiple species with static but different FRET efficiencies, single species with dynamic fluctuations between multiple FRET states, or a combination of the two cases. Burst Variance Analysis (BVA) is an analysis method for single molecule FRET experiments, developed to detect molecular dynamics~\cite{Torella_2011}. It has been successfully implemented to identify heterogeneities in FRET histograms due to dynamic processes of biomolecules in millisecond time scale~\cite{Torella_2011, Robb_2013}. BVA analysis consists of four steps: 1) slicing bursts into sub-bursts containing \textit{n} consecutive photons, 2) computing FRET efficiencies of each sub-burst, 3) calculating the empirical standard deviation ($s_E$) of sub-burst FRET efficiencies over the whole burst, and 4) comparing $s_E$ to an expected standard deviation based on shot noise limited distribution~\cite{Torella_2011}.