Guaranteed inference for probabilistic programs: a parallelisable, small-step operational approach
In the context of probabilistic programming languages, we put forward an approach to formal semantics and sampling-based inference with guarantees, centered on an action-based language equipped with a small-step operational semantics. We argue that this choice offers benefits in terms of clarity and...
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| Veröffentlicht in: | ACM transactions on probabilistic machine learning |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
30.09.2025
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| ISSN: | 2836-8924, 2836-8924 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | In the context of probabilistic programming languages, we put forward an approach to formal semantics and sampling-based inference with guarantees, centered on an action-based language equipped with a small-step operational semantics. We argue that this choice offers benefits in terms of clarity and effective, vectorized implementations. In measure-theoretic terms, a product of Markov kernels is used to formalize the small-step operational semantics. A trace semantics is also introduced based on a probability space of infinite sequences, along with a finite approximation theorem, relating the exact semantics to a truncated-execution semantics. This result directly leads to a sampling algorithm with guarantees, that can be efficiently SIMD-parallelized. Experiments conducted with an implementation based on TensorFlow show that our approach compares very favourably to state-of-the-art tools for probabilistic programming and inference.
Keywords : probabilistic programming, operational semantics, measure theory, Monte Carlo simulation, SIMD parallelism. |
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| ISSN: | 2836-8924 2836-8924 |
| DOI: | 10.1145/3769870 |