Functional Tensors for Probabilistic Programming Article Swipe
Fritz Obermeyer
,
Eli Bingham
,
Martin Jankowiak
,
Du Phan
,
Jonathan P. Chen
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.10775
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.10775
It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework. Noting that the versatility of modern automatic differentiation frameworks is based in large part on the unifying concept of tensors, we describe a software abstraction for integration --functional tensors-- that captures many of the benefits of tensors, while also being able to describe continuous probability distributions. Moreover, functional tensors are a natural candidate for generalized variable elimination and parallel-scan filtering algorithms that enable parallel exact inference for a large family of tractable modeling motifs. We demonstrate the versatility of functional tensors by integrating them into the modeling frontend and inference backend of the Pyro programming language. In experiments we show that the resulting framework enables a large variety of inference strategies, including those that mix exact and approximate inference.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1910.10775
- https://arxiv.org/pdf/1910.10775
- OA Status
- green
- Cited By
- 10
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2981145471
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https://openalex.org/W2981145471Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.1910.10775Digital Object Identifier
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Functional Tensors for Probabilistic ProgrammingWork title
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preprintOpenAlex work type
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enPrimary language
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2019Year of publication
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2019-10-23Full publication date if available
- Authors
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Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Du Phan, Jonathan P. ChenList of authors in order
- Landing page
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https://arxiv.org/abs/1910.10775Publisher landing page
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https://arxiv.org/pdf/1910.10775Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1910.10775Direct OA link when available
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Inference, Computer science, Probabilistic logic, Variable elimination, Abstraction, Variety (cybernetics), Theoretical computer science, Approximate inference, Type inference, Programming language, Artificial intelligence, Philosophy, EpistemologyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2024: 1, 2023: 2, 2022: 1, 2021: 5, 2020: 1Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.programming | 8, 117 |
| abstract_inverted_index.significant | 3 |
| abstract_inverted_index.strategies, | 133 |
| abstract_inverted_index.versatility | 26, 100 |
| abstract_inverted_index.--functional | 50 |
| abstract_inverted_index.parallel-scan | 81 |
| abstract_inverted_index.probabilistic | 7 |
| abstract_inverted_index.distributions. | 68 |
| abstract_inverted_index.differentiation | 30 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |