Approximations to Bayes

Abstract

Bayes’ Theorem tells us how to learn from data. We just need to assign our prior probability distribution for parameters or models and formulate the data likelihood function. But we have also seen that assigning a prior and likelihood is not always easy, and deriving a sample from the posterior distribution may require computationally demanding methods such as MCMC. So people keep searching for shortcuts where the Bayesian analysis can be made faster albeit perhaps a little bit less informative and accurate.