Bayesian machine learning often requires working with posterior distributions that have no known closed form. There are plenty of approximation methods, though many tend to be difficult to understand and implement or too computationally expensive. This post provides an overview of the Laplace Approximation: an exceptionally simple approach that is seeing a resurgence in recent deep learning papers.
