deletions | additions       diff --git a/Burst_Variance_Analysis.tex b/Burst_Variance_Analysis.tex  index d15a824..0647518 100644  --- a/Burst_Variance_Analysis.tex  +++ b/Burst_Variance_Analysis.tex 
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and has been successfully applied to identify biomolecular processes with   dynamics on the millisecond time-scale~\cite{Torella_2011, Robb_2013}.  The basic idea behind BVA is to subdivide bursts into contiguous burst chunks (sub-bursts)  comprising a fixed number $n$ of photons, and to compare the empirical variance of acceptor counts across all sub-bursts in a burst   with the theoretical shot-noise limited variance, as expected from a binomial distribution.  An empirical variance of burst chunks sub-bursts  larger than the shot-noise limited value indicates the presence of dynamics. Since the estimation of the burst chunks sub-bursts  variance is affected by uncertainty, BVA analysis provides and indication of an higher or lower probability  of observing dynamics.  In a FRET (sub-)population originating from a single static FRET efficiency,  the burst chunks sub-bursts  acceptor counts $N_a$ $n_a$  can be modeled as a binomial-distributed random variable $N_a \sim \operatorname{B}(n, E)$, where $n$ is the number of photons in each burst chunk and   $E$ is the estimated population FRET efficiency. Note that, without approximation, that  we can replace E with PR and use the uncorrected counts. This is possible because, regardless of the   molecular FRET efficiency, PR, since  the detected counts are partitioned between donor and acceptor channel according to a binomial distribution with a $p$ parameter equal to PR. PR (regardless of the   molecular FRET efficiency).  The only approximation done here is neglecting the presence background  (a reasonable approximation since the backgrounds counts are in general a very small fraction of the total counts). We refer the interested reader to~\cite{Torella_2011} for further discussion.  If $N_a$ follows a binomial distribution, the random variable $E = N_a/n$,