Summary of Learning Staged Trees From Incomplete Data, by Jack Storror Carter et al.
Learning Staged Trees from Incomplete Data
by Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 introduces novel algorithms for learning staged trees that can handle missing data within the model itself. Staged trees are probabilistic graphical models that represent non-symmetric independence via vertex coloring. Existing structural learning routines assume complete data, but this paper addresses the common problem of missing entries by characterizing the likelihood and pseudo-likelihoods for staged tree models with missing data. The authors implement a structural expectation-maximization algorithm that estimates the model directly from the full likelihood. A computational experiment demonstrates the performance of these algorithms, showing that they can effectively account for different missingness patterns when learning staged trees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn more about complex models called staged trees. These models help us understand how things are related without being exactly the same. The problem is that often we don’t have all the information we need, and this makes it hard to learn these models correctly. This paper introduces new ways to learn these models even when some of the information is missing. The authors use math and computer simulations to show that their methods work well. |
Keywords
» Artificial intelligence » Likelihood
