Semantics of distribution functions

Added on behalf of @sauer & @yoav.hollander

Suppose we define that len is soft-constrained to a normal distribution with some parameters

keep(soft len == random.normal(...))

If the specific value sampled contradicts some other hard constraints, do we try to sample again until we succeed?

Suggestion: look at probabilistic programming language