1. Machine Learning of the Prime Distribution
- Author
-
Kolpakov, Alexander and Rocke, A. Alistair
- Subjects
Computer Science - Information Theory ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Mathematics - Number Theory ,11N05 - Abstract
In the present work we use maximum entropy methods to derive several theorems in probabilistic number theory, including a version of the Hardy-Ramanujan Theorem. We also provide a theoretical argument explaining the experimental observations of Yang-Hui He about the learnability of primes, and posit that the Erd\H{o}s-Kac law would very unlikely be discovered by current machine learning techniques. Numerical experiments that we perform corroborate our theoretical findings., Comment: 10 pages; parts of arXiv:2308.10817 reworked and amended; author's draft; accepted in PLOS ONE
- Published
- 2024
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