If you want to get into probabilistic programming, I really recommend reading: https://probmods.org/ https://probmods.org/ It's my favorite book since SICP &

If you want to get into probabilistic programming, I really recommend reading: https://probmods.org/ https://probmods.org/ It's my favorite book since SICP & CTM. Some auxiliary materials: http://forestdb.org/ http://forestdb.org/ http://dippl.org/ http://dippl.org/ https://mhtess.github.io/bdappl/ https://mhtess.github.io/bdappl/ After that, for practical applications, Gen looks very promising. The MIT group already presented a very early prototype 2 years ago at PPAML. But if you don't understand the fundamentals (generative process, mixture model, hierarchical models, non-parametric models, etc) you will be lost. Note v1 of Probmods was written in Church (a Scheme dialect), which has some advantages to create meta-models.