Summary of Quotient Normalized Maximum Likelihood Criterion For Learning Bayesian Network Structures, by Tomi Silander et al.
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
by Tomi Silander, Janne Leppä-aho, Elias Jääsaari, Teemu Roos
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 quotient normalized maximum likelihood (qNML), an information-theoretic criterion for Bayesian network structure learning. Unlike factorized normalized maximum likelihood, qNML satisfies score equivalence, is decomposable, and lacks adjustable hyperparameters. The authors also propose a computationally efficient approximation method inspired by Szpankowski and Weinberger’s work. Experimental results on simulated and real-world data show that qNML produces parsimonious models with good predictive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn the best way to organize information. They create a new way to do this, called quotient normalized maximum likelihood (qNML). This method is better than others because it makes sure the results are fair and doesn’t require any special adjustments. The authors also found a quick way to calculate the answers using an idea from another researcher. They tested their method on fake and real data and showed that it works well, making good predictions. |
Keywords
» Artificial intelligence » Bayesian network » Likelihood
