deletions | additions       diff --git a/FRETBursts authorea latex/full_article.tex b/FRETBursts authorea latex/full_article.tex  index a265d12..4bf7432 100644  --- a/FRETBursts authorea latex/full_article.tex  +++ b/FRETBursts authorea latex/full_article.tex 
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\end{lstlisting}  Other useful attributes are \verb|aic| and \verb|bic| which contain  respectively statistics for  the Akaike information criterion (AIC)~\cite{akaike_new_1974} and the Bayes Information criterion (BIC)~\cite{schwarz_estimating_1978}.  AIC and BIC are general-purpose statistical criteria for comparing the  suitability of multiple non-nested  models and selecting according to  the most appropriate for a given dataset. data.  By penalizing models with higher number of parameters, these criteria   strike a balance between the need of achieving high goodness of fit   with the need of keeping the model complexity low to avoid overfitting.  diff --git a/fitting_2.tex b/fitting_2.tex  index 722cc9f..0d59751 100644  --- a/fitting_2.tex  +++ b/fitting_2.tex 
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\end{lstlisting}  Other useful attributes are \verb|aic| and \verb|bic| which contain  respectively statistics for  the Akaike information criterion (AIC)~\cite{akaike_new_1974} and the Bayes Information criterion (BIC)~\cite{schwarz_estimating_1978}.  AIC and BIC are general-purpose statistical criteria for comparing the  suitability of multiple non-nested  models and selecting according to  the most appropriate for a given dataset. data.  By penalizing models with higher number of parameters, these criteria   strike a balance between the need of achieving high goodness of fit   with the need of keeping the model complexity low to avoid overfitting.