the Laplace approximation

(40 minutes to learn)


The Laplace approximation is a way of approximating Bayesian parameter estimation and Bayesian model comparison. It is based on a second-order Taylor approximation of the log posterior around the MAP estimate, which results in a Gaussian approximation to the posterior.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Information Theory, Inference, and Learning Algorithms
A graudate-level textbook on machine learning and information theory.
Author: David MacKay


Supplemental resources (the following are optional, but you may find them useful)


See also