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English(EN) Tensor Network Moral Graph Recovery of Discrete Probability Distributions

新方法使用张量网络恢复离散概率分布图谱

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种使用全连接张量网络（FCTNs）重建离散概率分布道德图谱的新方法。该方法结合了核范数正则化的键校正，其中每个键矩阵都通过低秩校正进行调整。该方法证明，在特定假设下，具有零重建误差的最优FCTN将精确匹配道德图谱。对于近似场景，该技术提供了基于条件互信息的连续性的恢复界限，为解释优化键矩阵的有效图谱提供了直接途径。 AI

排序理由 该条目是一篇提交给arXiv的学术论文，详细介绍了一种新的统计方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新方法使用张量网络恢复离散概率分布图谱

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该条目是一篇提交给arXiv的学术论文，详细介绍了一种新的统计方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · \'A. Troyano Olivas, Chi-Hang Fred Fung, Hans H. Brunner, Momtchil Peev, Vicente Martin ·

    离散概率分布的张量网络道德图恢复

    arXiv:2609.09258v1 Announce Type: new Abstract: We present a method for recovering the moral graph of a causal DAG from a probability distribution over discrete variables, using fully connected tensor networks (FCTNs) with nuclear-norm-regularized bond corrections. Each bond matr…

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