- #1
Avatrin
- 245
- 6
Hi
I've been googling maximum likelihood estimation. While I do understand how to compute it, I don't understand why maximizing the likelihood function will give us a good estimate of the actual parameter.
In some cases, like the normal distribution, it seems almost obvious. However, in the more general case, I don't know why it is true.
So, I have two questions:
How much knowledge do I need to prove that the maximum of the likelihood function is an estimator of the actual parameter?
Is there a relative intuitive explanation for why this method gives us a good estimate for the actual parameter?
I've been googling maximum likelihood estimation. While I do understand how to compute it, I don't understand why maximizing the likelihood function will give us a good estimate of the actual parameter.
In some cases, like the normal distribution, it seems almost obvious. However, in the more general case, I don't know why it is true.
So, I have two questions:
How much knowledge do I need to prove that the maximum of the likelihood function is an estimator of the actual parameter?
Is there a relative intuitive explanation for why this method gives us a good estimate for the actual parameter?