distribution - Gaussian fit to discrete values -

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i'm new in c++ , i'm struggling this. have matrix discrete values adapt gaussian distribution. need algorithm in c++ define parameters of gaussian fit. suggestions or help?? 1 of vectors in data set {14, 3, 2, 83, 263, 236, 101, 27, 7, 13, 12, 8} increment 2 ns between each value.

thanks lot

as bathsheba said, that's needed characterize gaussian (a.k.a. normal) distribution mean , variance. can estimate these sample mean , sample variance s^2 of data, respectively.

however, shouldn't so! histogram , normal quantile plot of data show nothing gaussian distribution. gaussians should have histogram bell-shaped , symmetric. data, despite small sample size, skewed exponential distribution doesn't particularly fit. in normal quantile plot, if data gaussian points fall along relatively straight line (the red line best fit straight line values) , within dotted-line boundaries. data aren't vaguely close being fit normal distribution.

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