- Tags:: [[statistics|Statistics]] - In Bayesian statistics, it is like the [[Maximum likelihood ml|Maximum Likelihood Ml]] with a prior distribution: - $\theta_{MAP} = \underset{\theta}{\mathrm{arg max}} P(\theta|X)$ - Which is equivalent, by the Bayes theorem, to: - $\theta_{MAP} = \underset{\theta}{\mathrm{arg max}} P(X|\theta)P(\theta)$ - Where it is easy to see that the MLE is a special case of the MAP with a uniform prior. - Reference:: Wikipedia