Summary of Probabilistic Circuits For Cumulative Distribution Functions, by Oliver Broadrick and William Cao and Benjie Wang and Martin Trapp and Guy Van Den Broeck
Probabilistic Circuits for Cumulative Distribution Functions
by Oliver Broadrick, William Cao, Benjie Wang, Martin Trapp, Guy Van den Broeck
First submitted to arxiv on: 8 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 The proposed probabilistic circuits (PCs) can succinctly represent multivariate probability distributions and support efficient probabilistic inference. This study shows that PCs computing cumulative distribution functions (CDFs) are equivalent to those computing probability mass or density functions (PMF or PDF), with polynomial-time transformations possible between the two representations. The findings also extend to finite discrete variables, where a modified encoding allows for efficient transformation between PMFs and CDFs. Additionally, smooth and decomposable PCs computing PDFs and CDFs can be efficiently transformed into each other by modifying only the leaves of the circuit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Probabilistic circuits are a new way to understand complex probability distributions. In this study, researchers showed that these circuits can compute two different types of information: the probability of something happening (PDF) or the chance that something has happened up until a certain point (CDF). They found that it’s possible to transform one type of information into the other quickly and efficiently. This is important because it means we can use these circuits to solve problems in areas like machine learning, statistics, and data science. |
Keywords
» Artificial intelligence » Inference » Machine learning » Probability
