This papers shows with a series of examples how probabilistic languages can be integrated into deep learning strategies. In particular shows how to model a multilayer perceptron unit (MPL) and a variational autoencoder (VAE).
**Conceptual Categorisation of Probabilistic Programming Languages (PPL)**
**Conceptual Categorisation of Deep Probabilistic Programming Languages (PPL)**
Probabilistic programming languages (PPLs) aim to express a probabilistic model as program. This abstraction is motivated by the idea that probabilistic models can become very difficult whereas implementing programs is usually a very standardised and thus easy to do.
