Inference for max-linear Bayesian networks with noise

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.

@article{adams2025_2505.00229,
  title={ Inference for max-linear Bayesian networks with noise },
  author={ Mark Adams and Kamillo Ferry and Ruriko Yoshida },
  journal={arXiv preprint arXiv:2505.00229},
  year={ 2025 }
}