I like this passage: In this and the next two chapters of this introduction we will present the key ideas of probabilistic programming using a carefully designe

I like this passage: In this and the next two chapters of this introduction we will present the key ideas of probabilistic programming using a carefully designed first-order probabilistic programming language (FOPPL). The FOPPL includes most common features of programming languages, such as conditional statements (e.g. if), primitive operations (e.g. +,-, etc.), and user-defined functions. The restrictions that we impose are that functions must be first order, which is to say that functions cannot accept other functions as arguments, and that they cannot be recursive. These two restrictions result in a language where models describe distributions over a finite number of random variables. In terms of expressivity, this places the FOPPL on even footing with many existing languages and libraries for automating inference in graphical models with finite graphs. This gives a nice picture of what's happening. At the same time, does this mean that in the end, you're basically operating on a single distribution with only a few canned global transformations?