Towards representation agnostic probabilistic programming
Fuente:
arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866914224652943360 |
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| author | Fenske, Ole Popko, Maximilian Bader, Sebastian Kirste, Thomas |
| author_facet | Fenske, Ole Popko, Maximilian Bader, Sebastian Kirste, Thomas |
| contents | Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of their underlying representation. This enables representation-agnostic probabilistic programming where users can freely mix different representations (e.g. discrete tables, Gaussians distributions, sample-based approaches) within a single unified framework, allowing practical inference in complex hybrid models that current toolkits cannot adequately express. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23740 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards representation agnostic probabilistic programming Fenske, Ole Popko, Maximilian Bader, Sebastian Kirste, Thomas Programming Languages Artificial Intelligence Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of their underlying representation. This enables representation-agnostic probabilistic programming where users can freely mix different representations (e.g. discrete tables, Gaussians distributions, sample-based approaches) within a single unified framework, allowing practical inference in complex hybrid models that current toolkits cannot adequately express. |
| title | Towards representation agnostic probabilistic programming |
| topic | Programming Languages Artificial Intelligence |
| url | https://arxiv.org/abs/2512.23740 |