An entropy for Boolean independence
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918204807315456 |
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| author | Pan, Kewei |
| author_facet | Pan, Kewei |
| contents | In this article, we aim to define a Boolean entropy notion parallel to the framework of free entropy proposed by Voiculescu. Motivated by the work of Lenczewski and the work of Cébron & Gillers, we mainly investigated two random matrix models (the Gaussian Symmetric Block model and the Conditioned GUE model), in which asymptotic Boolean independence appears. We showed a large deviation principle for both models. As a result, the two rate functions coincide up to scaling and are minimized by the Rademacher distribution. Therefore, we refer to the logarithmic integral in the rate function as Boolean entropy. Finally, we proved this logarithmic integral is maximized by the Rademacher distribution and monotone along the Boolean Central Limit Theorem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07907 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An entropy for Boolean independence Pan, Kewei Probability In this article, we aim to define a Boolean entropy notion parallel to the framework of free entropy proposed by Voiculescu. Motivated by the work of Lenczewski and the work of Cébron & Gillers, we mainly investigated two random matrix models (the Gaussian Symmetric Block model and the Conditioned GUE model), in which asymptotic Boolean independence appears. We showed a large deviation principle for both models. As a result, the two rate functions coincide up to scaling and are minimized by the Rademacher distribution. Therefore, we refer to the logarithmic integral in the rate function as Boolean entropy. Finally, we proved this logarithmic integral is maximized by the Rademacher distribution and monotone along the Boolean Central Limit Theorem. |
| title | An entropy for Boolean independence |
| topic | Probability |
| url | https://arxiv.org/abs/2505.07907 |