Summary of Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors, by Yidou Weng et al.
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors
by Yidou Weng, Finale Doshi-Velez
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to learning Semi-parametric relationships in Expert Bayesian Networks (SEBNs) with linear parameter and structure constraints. The proposed model combines Gaussian Processes and Horseshoe priors to introduce minimal nonlinear components, prioritizing the modification of expert graphs over adding new edges. To address identifiability issues and enhance interpretability, the authors optimize differential Horseshoe scales. The approach is evaluated on synthetic and real-world datasets, including UCI Liver Disorders, using metrics like structural Hamming Distance and test likelihood, demonstrating state-of-the-art performance compared to existing semi-parametric Bayesian Network models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to learn about complex networks. It’s called Expert Bayesian Networks (EBNs). The EBNs have rules that help us understand how things are connected. The scientists in this study created a new model that can learn from the data and make better connections between things. They tested their model on some real-world datasets and showed it works better than other models. This could be important for people who need to understand complex networks, like doctors trying to figure out how diseases spread. |
Keywords
* Artificial intelligence * Bayesian network * Likelihood
