'mle' - can we use this command for maximizing log-likelihood estimation of a vector input, which is normally distributed?
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I have a vector of particular length, normally (Gaussian) distributed, for which I want to maximize log-likelihood estimation. I have directly given that vector to 'mle' command. But the output I got was not the exact thing I needed. So, how to calculate the log-likelihood estimation? and how to maximise it? It would be a great help if someone could help me in this regard....
Mayank Sengar on 30 Aug 2022
You can use mle function to calculate the maximum likelihood estimation. Since the logarithmic function is monotonic, maximizing the likelihood is same as maximizing the log of the likelihood. Therefore, taking logarithm of the result will give you the maximum log likelihood estimation.