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in this video we dont look at how to use Monte Carlo simulation to understand a peak model and its stability with respect to noise when we construct a peak model we define a set of fitting parameters that are positioned fourth half maximum an area and we may introduce constraints between these parameters that will guide a peak model we also use different line shapes and these line shapes also represent a shape constraint on how data are fitted we have to choose the number of components and then we have to fit these components to the data using an optimization routine now the idea of the optimization routine is that it determines the position full width half maximum and area so these components reproduce the data with the greatest precision possible and when we perform optimization there may be local minima that are deep or there may be shallow and depending on the nature of these minima then the peak model will either be stable with respect to noise so if we give a perturbation to th