deletions | additions       diff --git a/Burst_Variance_Analysis.tex b/Burst_Variance_Analysis.tex  index 0e5aa2f..2ca7479 100644  --- a/Burst_Variance_Analysis.tex  +++ b/Burst_Variance_Analysis.tex 
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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 of  all sub-bursts in a burst burst,  with the theoretical shot-noise-limited variance, as expected from a binomial distribution. variance.  An empirical variance of sub-bursts larger than the shot-noise limited value indicates  the presence of dynamics. Since the estimation of the sub-bursts variance is affected  by uncertainty, BVA analysis provides and indication of an higher or lower probability 
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In a FRET (sub-)population originating from a single static FRET efficiency,  the sub-bursts acceptor counts $n_a$ can be modeled as a binomial-distributed random variable   $N_a \sim \operatorname{B}(n, E_p)$, where $n$ is the number of photons in each sub-burst and   $E_p$ is the estimated population proximity-ratio. proximity-ratio (PR).  Note that we can use the PR because, regardless of the molecular FRET efficiency,   the detected counts are partitioned between donor and acceptor channels according to  a binomial distribution with success probability equal to the PR.