mlsanityData to 14 Oct 2026

Inference for max-linear Bayesian networks with noise

arXiv 2505.00229 ↗First posted 1 May 2025stat.ML cs.LG math.OCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 26 references that are in the map. First posted on arXiv 1 May 2025.

Builds on

5 of 26 references.

References · 5

  1. 2015Extremes on river networkscited by 9
  2. 2021Estimating a Directed Tree for Extremescited by 5
  3. 2016A continuous updating weighted least squares estimator of tail dependence in high dimensionscited by 3
  4. 2019Bayesian Networks for Max-linear Modelscited by 3
  5. 2019Estimating an Extreme Bayesian Network via Scalingscited by 2

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Citing papers per year: 2025: 1.

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  1. 2025A PC Algorithm for Max-Linear Bayesian Networkscited by 0