Maximum a Posteriori Map

• Tags:: Statistics

• In Bayesian statistics, it is like the Maximum Likelihood Ml with a prior distribution:

  • ${\theta }_{MAP}=\underset{\theta }{\mathrm{argmax}}P(\theta |X)$

• Which is equivalent, by the Bayes theorem, to:

  • ${\theta }_{MAP}=\underset{\theta }{\mathrm{argmax}}P(X|\theta )P(\theta )$

• Where it is easy to see that the MLE is a special case of the MAP with a uniform prior.

• Reference:: Wikipedia