# How to fit a Gaussian distribution with outlier data points?

I have a data set which consists of large number of data points. However, there are some outlier points that can be considered be noise. If I include all data points to approximate the Gaussian distribution, the standard deviation is apparently larger than expected. How can I fit this data set with a Gaussian distribution and get accurate mean and standard deviation ignoring these noise points.

Thanks

• is it a univariate or a multivariate gaussian you are trying to fit? – user603 Nov 11 '13 at 15:12
• a univariate gaussian, not mixture gaussian – user22062 Nov 12 '13 at 5:27
• multivariate is not the same as mixture. Assuming you meant univariate, then, your question is a duplicate of this one – user603 Nov 12 '13 at 9:15
• The question to ask yourself: how do you know, that they are outliers? – Tim Jan 3 '17 at 12:44

• -1 This has nothing to do with fitting distribution to data. Fitting distribution to data $\ne$ fitting curve to points. – Tim Feb 4 '17 at 17:14