提出带噪声的极值因果模型推断方法，实现边参数的正态分布估计。
Inference for max-linear Bayesian networks with noise
- 基于对数变换与max-plus代数构建噪声型最大线性贝叶斯网络
- 证明有向无环图中每条边的参数估计量服从正态分布
- 结合EM算法与二次优化实现高效计算，适用于极端事件建模
最大线性贝叶斯网络（MLBNs）为极值场景下的因果推断提供了强大框架。本文在给定拓扑结构下，通过取对数将带有噪声参数的MLBNs置于max-plus代数框架中进行分析。我们证明了有向无环图（DAG）中每条边的参数估计量呈正态分布。论文最后通过期望最大化（EM）算法和二次优化进行了计算实验，验证了方法的有效性。
原文摘要 · Abstract (English)
Max-Linear Bayesian Networks (MLBNs) provide a powerful framework for causal inference in extreme-value settings; we consider MLBNs with noise parameters with a given topology in terms of the max-plus algebra by taking its logarithm. Then, we show that an estimator of a parameter for each edge in a directed acyclic graph (DAG) is distributed normally. We end this paper with computational experiments with the expectation and maximization (EM) algorithm and quadratic optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。