Inference for max-linear Bayesian networks with noise
Machine Learning
2025-05-02 v1 Machine Learning
Optimization and Control
Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.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}
}
Comments
18 pages, 10 figures. Short version to appear in the proceedings of the 13th Workshop on Uncertainty Processing