uncertainty/deep-ppl.md

@@ -6,10 +6,14 @@ This papers shows with a series of examples how probabilistic languages can be i
 
 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 and involve intractable integrals, whereas implementing programs is usually a very standardised and thus easy to do. 
 
-As illustrated below the user specified probabilistic program specifies how to generate output data by sampling from a latent probability distribution. The compiler processes this program by checking for type errors and translate the program into an inference procedure. During inference, the latent probability distribution is fitted to the observed data. The output is thus the probabilistic model including uncertainty measures. This uncertainty measure makes PPL models particularly interesting for the machine learning community.
+As illustrated below the user specified probabilistic program specifies how to generate output data by sampling from a latent probability distribution. The compiler processes this program by checking for type errors and translates the program into an inference procedure. During inference, the latent probability distribution is fitted to the observed data. The output is thus the probabilistic model including uncertainty measures. This uncertainty measure makes PPL models particularly interesting for the machine learning community.
 
 ![PPL-overview](uploads/8746dead4ee50539540a6da708a9ceb5/PPL-overview.png)
 
+**How to Set-up a PPL Program**
+
+The goal is to infer a probabilistic model U+03B8
+
 **Conceptual Categorisation of Deep Probabilistic Programming Languages (PPL)**
 
 As illustrated in the Figure below, deep PPLs combine three existing concepts into one new concept: "programming languages", "Bayesian statistics" and "deep learning".