Tractable uncertainty for structure learning
Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate posterior inference that relies on probabilisti...
| 主要な著者: | , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
Journal of Machine Learning Research
2022
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