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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09853 |
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| _version_ | 1866915546135527424 |
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| author | Saul, Bradley |
| author_facet | Saul, Bradley |
| contents | Beginning in the 1970s, statistician-cum-logician Per Martin-Löf wrote a series of papers developing what became Martin-Löf type theory, realizing a system where the distinction between mathematics and programming disappears. Inspired by this vision, this paper introduces dependent type theory (of which Martin-Löf type theory is an example) to a statistical audience. Examples from statistics and probability theory demonstrate how dependent type theory and an algebraic perspective can unify the theoretical and computational concerns of statistics, ensuring rigorous, machine-checked proofs and executable software. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09853 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Great expectations: Unifying Statistical Theory and Programming Saul, Bradley Computation Beginning in the 1970s, statistician-cum-logician Per Martin-Löf wrote a series of papers developing what became Martin-Löf type theory, realizing a system where the distinction between mathematics and programming disappears. Inspired by this vision, this paper introduces dependent type theory (of which Martin-Löf type theory is an example) to a statistical audience. Examples from statistics and probability theory demonstrate how dependent type theory and an algebraic perspective can unify the theoretical and computational concerns of statistics, ensuring rigorous, machine-checked proofs and executable software. |
| title | Great expectations: Unifying Statistical Theory and Programming |
| topic | Computation |
| url | https://arxiv.org/abs/2510.09853 |