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MLE vs MAP (conceptual)
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507.
MLE Definition
easy
What does MLE stand for and what does it estimate?
A
Maximum Likelihood Estimation; it finds the parameter value that maximizes the posterior distribution
B
Minimum Loss Estimation; it finds the parameter value that minimizes prediction error on training data
C
Marginal Likelihood Estimation; it integrates over all possible parameter values to find the best model
D
Maximum Likelihood Estimation; it finds the parameter value that maximizes the probability of the observed data
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