This might not answer your question directly, but it is perhaps useful to know that the probabilistic programming approach can also be applied in non-probabilistic (non-statistical) ways.
I developed a ranked programming language, which is like a probabilistic programming language without probabilities. Instead, you state how your program normally behaves and how it may exceptionally behave. Conceptually it's very similar to probabilistic programming, but the underlying uncertainty formalism is replaced with ranking theory, which works with integer-valued degrees of surprise.
You can find an implementation of this idea (based on Scheme/Racket) here:
"We focus on the case where we know what is normal and what is exceptional, but where the probabilistic meaning of these terms is unknown or irrelevant." Interesting stuff!
This might not answer your question directly, but it is perhaps useful to know that the probabilistic programming approach can also be applied in non-probabilistic (non-statistical) ways. I developed a ranked programming language, which is like a probabilistic programming language without probabilities. Instead, you state how your program normally behaves and how it may exceptionally behave. Conceptually it's very similar to probabilistic programming, but the underlying uncertainty formalism is replaced with ranking theory, which works with integer-valued degrees of surprise.
You can find an implementation of this idea (based on Scheme/Racket) here:
https://github.com/tjitze/ranked-programming
For more detailed information check the paper linked to on that page.