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What is MLE and MAP Estimators in ML about?

1. The maximum likelihood estimator (MLE) for the mean of a univariate normal distribution with known variance σ^2 and samples x1, ..., xN is the sample mean. 2. The maximum a posteriori (MAP) estimator assumes a normal prior on the mean with parameters ν and β^2. The MAP estimator is a weighted average of the sample mean and the prior mean ν, with the weights depending on σ^2 and β^2. 3. As the number of samples N goes to infinity, both the MLE and MAP estimators converge to the true mean.

Author

saumya

Language

EN