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There are many different probabilistic programming languages that are specialized to specific kinds of probabilistic programs. From a usability and scalability perspective, this is undesirable: today, probabilistic programmers are forced up-front to decide which language they want to use and cannot mix-and-match different languages for handling heterogeneous programs. To rectify this, we seek a foundation for sound interoperability for probabilistic programming languages: just as today’s Python programmers can resort to low-level C programming for performance, we argue that probabilistic programmers should be able to freely mix different languages for meeting the demands of heterogeneous probabilistic programming environments. As a first step towards this goal, we introduce MultiPPL, a probabilistic multi-language that enables programmers to interoperate between two different probabilistic programming languages: one that leverages a high-performance exact discrete inference strategy, and one that uses approximate importance sampling. We give a syntax and semantics for MultiPPL, prove soundness of its inference algorithm, and provide empirical evidence that it enables programmers to perform inference on complex heterogeneous probabilistic programs and flexibly exploits the strengths and weaknesses of two languages simultaneously.
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What Happens When Students Switch (Functional) Languages (Experience Report)
When novice programming students already know one programming language and have to learn another, what issues do they run into? We specifically focus on one or both languages being functional, varying along two axes: syntax and semantics. We report on problems, especially persistent ones. This work can be of immediate value to educators and also sets up avenues for future research.
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- Award ID(s):
- 2227863
- PAR ID:
- 10602322
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 7
- Issue:
- ICFP
- ISSN:
- 2475-1421
- Format(s):
- Medium: X Size: p. 796-812
- Size(s):
- p. 796-812
- Sponsoring Org:
- National Science Foundation
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