AI engineering glossary · Statistics inference
What is MAP (maximum a posteriori estimation)?
Maximum a posteriori estimation (also called MAP): A method that picks the parameter value with the highest posterior probability, combining the data likelihood with a prior belief. It equals maximum likelihood with an added penalty term, which is why L2 weight decay corresponds to a Gaussian prior.
Maximum a posteriori estimation: an example
Adding a normal prior centred at zero to a regression's weights and maximising gives the same fit as ridge regression.
Related terms
More Statistics inference terms
- Minimum detectable effect
- Multiple comparisons problem
- Non-parametric test
- Novelty effect
- Null hypothesis
- One-tailed and two-tailed tests
- P-hacking
- P-value
Where you learn it
Maximum a posteriori estimation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.