Originally Posted by: Jean Giraud
Problem is that we don't know what you are looking for.
Are you looking for the best fit to your data set ???
If so, minute job.
Please, don't hesitate ... Jean
So I wrote down what I needed at the very beginning.
There is a data set (two rows of numbers).
Basically, they should be random sets.
However, in reality, two or even three different data sets are mixed there.
I need to separate them - to identify those normal distributions that correspond to pure data sets (not mixed data sets).
In the previous replica of for
overlord, I posted the full calculation method.
Now only one question remains. Iterating through 9 parameters requires a lot of time. And the calculation is carried out with an accuracy that is superfluous for my purposes.
Sq and
Sqmin are calculated with an accuracy of 15 decimal places. And I would have more than enough 8 or 9 digits after the decimal point.
This would significantly reduce the calculation time.
And here is the Pearson correlation coefficient I have 0.9999, whereas I would have more than enough 0.9
On the other hand, perhaps the Pearson coefficient should not be worsened.
Here in that calculation (in the previous replica) I had an error after integrating 1.5%, and I slightly knocked down the parameters, and it increased to 2.4%.
And this is more important than the Pearson coefficient. I would not count it at all, but I will definitely be asked a question about this coefficient.
There the definite integral is taken
WW(x). And it is equal to 0.9658.
And initially I have a histogram
g2(x), and if you take the integral of it, it will be equal to 0.99.
And in my case, this is more important than the Pearson coefficient.