Bayesian Neural Network Series Post 2: Background Knowledge

This post is the second post in an eight-post series of Bayesian Convolutional Networks. The posts will be structured as follows:

1. Need for Bayesian Neural Networks
2. Background knowledge needed to understand Bayesian Networks better
3. Some recent work in the field of Bayesian Neural Networks
4. Bayesian Convolutional Networks using Variational Inference
5. Build your own Bayesian Convolutional Network in PyTorch
6. Uncertainty estimation in a Bayesian Neural Network
7. Model Pruning in a Bayesian Neural Network
8. Applications in other areas (Super Resolution, GANs and so on..)

It is highly recommended to read the first post about the need for Bayesian Networks before proceeding further. Also, check out the post ‘Why the World needs a Bayesian perspective’.

Let’s start this post by breaking Bayesian Neural Networks into Bayesian and Neural Networks.

Bayesian inference forms an important part of statistics and probabilistic machine learning. It is based on Bayes’ theorem given by famous…