https://github.com/majianthu/eval
Code for the Paper "Evaluating Independence and Conditional Independence Measures"
https://github.com/majianthu/eval
benchmarking conditional-independence-test copula-entropy distance-correlation independence-tests kernel-methods
Last synced: 3 months ago
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Code for the Paper "Evaluating Independence and Conditional Independence Measures"
- Host: GitHub
- URL: https://github.com/majianthu/eval
- Owner: majianthu
- License: apache-2.0
- Created: 2022-05-17T10:50:02.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-21T08:38:47.000Z (almost 2 years ago)
- Last Synced: 2024-04-22T04:55:46.223Z (almost 2 years ago)
- Topics: benchmarking, conditional-independence-test, copula-entropy, distance-correlation, independence-tests, kernel-methods
- Language: R
- Homepage: https://arxiv.org/abs/2205.07253
- Size: 37.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
### Evaluating Independence and Conditional Independence Measures
Code for the Paper "Evaluating Independence and Conditional Independence Measures" which is available at [here](https://arxiv.org/abs/2205.07253) on arXiv. The [heart disease data](https://archive.ics.uci.edu/ml/datasets/heart+disease), [wine quality data](https://archive.ics.uci.edu/ml/datasets/wine+quality), and [Beijing air data](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data) from the UCI machine learning repository are used.