How to get minimum value of given function by steepest-descent method with one-dimensional linear search?
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given function:
f=exp(-(x1-mul1)^2/(2*sigma1^2))*exp(-(x2-mul2)^2/(2*sigma2^2))*...
*exp(-(xn-muln)^2/(2*sigman^2));
where X{x1,x2,…,xn}is given by input data set, we can see X as known value. (muli,sigmai) is the parameter corresponding to xi. Looking forward your coding guidance,thank you!
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Torsten
on 24 Nov 2016
I don't really know what you are trying to do.
Maximum Likelihood estimation of the parameters of an n-dimensional Gaussian ?
Best wishes
Torsten.
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