Maximum likelihood means taking the outcome with the highest probability relative to everything else, correct? This isn’t really desirable since the outcome with the highest probability, might still have very low absolute probability.
Maximum likelihood means taking the outcome with the highest probability relative to everything else, correct?
No, not at all, what you are talking about is called the mode of the distribution.
Why don’t you look at the links in my post?
a maximum-likelihood estimate is often defined to be a zero of the derivative of the likelihood function with respect to the parameter
And the equation.
I don’t see how it’s different than the mode. Even the graphs show it as being the same: 1 2.
I don’t see how it’s different than the mode
Think about a bimodal distribution, for example. But in any case, we’re talking about M-estimates, weren’t we?
Maximum likelihood means taking the outcome with the highest probability relative to everything else, correct? This isn’t really desirable since the outcome with the highest probability, might still have very low absolute probability.
No, not at all, what you are talking about is called the mode of the distribution.
Why don’t you look at the links in my post?
And the equation.
I don’t see how it’s different than the mode. Even the graphs show it as being the same: 1 2.
Think about a bimodal distribution, for example. But in any case, we’re talking about M-estimates, weren’t we?