Seonglae ChoMaximum A Posteriori
Intuitively, MLE finds the theta that maximizes the probability of the data, while MAP finds the most probable model given the data. Since MAP includes the prior term when divided by Bayes denominator, it is considered a generalized form of MLE.
- priori mean ‘from the earlier’
- posteriori means ‘from the later’
finds the parameters maximizing a posteriori distribution
assume also has some distribution and find optimal
We assume a zero-mean Gaussian prior with covariance Σ for parameters