Seonglae ChoMaximum A Posteriori
- 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
with called the regularization parameter and is a measure of complexity
If we use the Log-likelihood function, a common penalty is to use where is the prior. By setting , and ignoring the which does not depend on .
When we use a log form of Bayes Theorem, minimizing this is equivalent to maximizing the log posterior: MAP