Maximum Likelihood Estimator (MLE) vs. Maximimum a Posteriori Estimator (MAP)

What's the difference?

We want to estimate the best parameters Theta for our model given some data D.

The MLE chooses

Theta_MLE = argmax_{Theta} P(D|Theta)

i.e. the parameters that maximize the likelihood.

The MAP chooses

Theta_MAP = argmax_{Theta} P(D|Theta) P(Theta) = arg_max{Theta} P(Theta|D)

So in contrast to the MLE, the MAP estimate considers the a-priori probability when choosing the most probable model parameters Theta as well.

Since P(D|Theta) P(Theta) is proportional to P(Theta|D), which is the posterior probability in Bayes theorem, it is also called the Maximum A-Posteriori estimate.

An important difference between both estimators is:

  • MLE tends to overfit the parameters Theta the data
  • MAP does not tend to overfit, since it uses the a-priori probabilities of the parameters Theta when choosing its estimate

Videos: MLE

intro to MLE with pros/cons, part I

intro to MLE with pros/cons, part II

Videos: MAP

intro to MAP with pros/cons

public/maximum_likelihood_estimator_mle_vs._maximimum_a_posteriori_estimator_map.txt · Last modified: 2014/01/02 10:07 (external edit) · []
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