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Therefore weights proportional to $n_{ti}$ represent a natural choice (see SI XXX).  In general, such a weighting scheme is used for building efficient estimators for a population  parameter (e.g. $E_p$). But, it can also be used to build weighted histograms or Kernel Density  Estimation (KDE) plots which exhibit FRET subpopulations with optimal minimal  width, yielding more accurate fit of peaks positions and better resolution of nearby peaks  (compared to corresponding non-weighted plots using the same burst-size threshold).  Traditionally, for optimal results when not using weights, the width of