uncertainty/deep-ppl.md

@@ -13,6 +13,10 @@ As illustrated below the user specified probabilistic program specifies how to g
 **How to Set-up a PPL Program**
 
 The goal is to infer a probabilistic model θ for observed data x<sub>1</sub>, ..., x<sub>n</sub>: p(θ|x<sub>1</sub>, ..., x<sub>n</sub>). 
+This is done by using Bayesian statistics and requires usually the following steps:
+1.  Define a probabilistic model (e.g. Bernulli distribution) by (i) setting up the parameter(s) for the model (θ; i.e. the latent random variable(s)) and (ii) seting up a (discrete) set of random variables sampled from the model (x<sub>1</sub>, ..., x<sub>n</sub>; i.e. observed variables) used to check the quality of the model θ.
+2.  Specify the measured data (i.e. import real world data)
+3.  Run Inference using the model and the data (e.g. by Monte Carlo Sampling)
 
 **Conceptual Categorisation of Deep Probabilistic Programming Languages (PPL)**