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how to understand taking a derivative in using MLE?

I was reading an article about MLE and MAP lately, and need to clarify a little bit. https://wiseodd.github.io/techblog/2017/01/01/mle-vs-map/ So, it talks about "This is MLE, as, if we take the derivative of the Gaussian function with respect to the mean and variance, and maximizing it (i.e. setting the derivative to zero), what we get is functions that are calculating sample mean and sample variance.". So, here, is it taking the derivative of the Gaussian density function or the Gaussian cumulative distribution function? I guess it should be Gaussian density function. Is it correct?

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updated by DBScan • DBScan • written by 2001linana • 2001linana

1 answer

Pretty sure you take the derivative from the probability density function (PDF) and not the CDF.

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written by DBScan • DBScan

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