# Variational inference from scratch

## September 16, 2019

In the posts Expectation Maximization and Bayesian inference; How we are able to chase the Posterior, we laid the mathematical foundation of variational inference. This post we will continue on that foundation and implement variational inference in Pytorch. If you are not familiar with the basis, I’d recommend reading these posts to get you up to speed.
This post we’ll model a probablistic layer as output layer of a neural network.
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