Inference for max-linear Bayesian networks with noise
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915268187389952 |
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| author | Adams, Mark Ferry, Kamillo Yoshida, Ruriko |
| author_facet | Adams, Mark Ferry, Kamillo Yoshida, Ruriko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inference for max-linear Bayesian networks with noise Adams, Mark Ferry, Kamillo Yoshida, Ruriko Machine Learning Optimization and Control Statistics Theory 14T90, 62A09, 62H30, 90C20, 90C90 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. |
| title | Inference for max-linear Bayesian networks with noise |
| topic | Machine Learning Optimization and Control Statistics Theory 14T90, 62A09, 62H30, 90C20, 90C90 |
| url | https://arxiv.org/abs/2505.00229 |