Bayesian Inference, MAP & MLE
3. Bayesian Inference, MAP & MLEΒΆ
In the following sections, we explain the concepts of Bayesian inference, the maximum a-posteriori method (MAP) and maximum likelihood estimation (MLE).
Bayesian inference uses Bayes theorem to update the probabilities as more data/evidence is available.
In many applications, it is not possible to perform full Bayesian inference, since the required terms can not be computed explicitly. To overcome this problem, MLE and MAP are useful tools in many cases.
As an introduction, we discuss the coin toss example in detail. In particular, the difference between the Bayesian and the frequentists approach is illustrated.