Q 27. In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because ___________
1. The normalizing constant is always very close to 1
2. The normalizing constant only has a small impact on the maximum likelihood
3. The normalizing constant is often zero and can cause division by zero
Ans: 4
Exp: (Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.
