Researchers have developed a novel method for reconstructing the moral graph of discrete probability distributions using fully connected tensor networks (FCTNs). This approach incorporates nuclear-norm-regularized bond corrections, where each bond matrix is adjusted with a low-rank correction. The method proves that under specific assumptions, an optimal FCTN with zero reconstruction error will precisely match the moral graph. For approximate scenarios, the technique provides recovery bounds based on the continuity of conditional mutual information, offering a direct way to interpret the effective graph from optimized bond matrices. AI
RANK_REASON The item is an academic paper submitted to arXiv detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
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- C_{ij} = U_{ij}V_{ij}^ op
- Conditional mutual information
- CORE Recommender
- DagsHub
- Fannes-Audenaert continuity
- FCTNs
- Gotit.pub
- Hugging Face
- Influence Flower
- nuclear-norm-regularized bond corrections
- ScienceCast
- Tensor Network Moral Graph Recovery of Discrete Probability Distributions
- variational Frobenius norm penalty
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