A compilation target for probabilistic programming languages

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be im-plemented in any programming language by cre-ative use of standardized operating system func-tionality including processes, forking, mutexes, and shared memory. Exploiting this we have de-fined, developed, and tested a probabilistic pro-gramming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard com-pilers and linked to operating system libraries yielding an efficient, scalable, portable proba-bilistic programming compilation target. This opens up a new hardware and systems research path for optimizing probabilistic programming systems. 1