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Mutual Information between Discrete and Continuous Data Sets

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  • Brian C Ross

Abstract

Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with “binning” when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen–Shannon divergence of two or more data sets.

Suggested Citation

  • Brian C Ross, 2014. "Mutual Information between Discrete and Continuous Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-5, February.
    • Handle: RePEc:plo:pone00:0087357
    DOI: 10.1371/journal.pone.0087357
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    Cited by:

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    3. Xin Dang & Dao Nguyen & Yixin Chen & Junying Zhang, 2021. "A new Gini correlation between quantitative and qualitative variables," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1314-1343, December.
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    7. Ao Kong & Robert Azencott & Hongliang Zhu & Xindan Li, 2020. "Pattern recognition in micro-trading behaviors before stock price jumps: A framework based on multivariate time series analysis," Papers 2011.04939, arXiv.org, revised Feb 2021.
    8. Kandula, Shanthan & Krishnamoorthy, Srikumar & Roy, Debjit, 2020. "A Predictive and Prescriptive Analytics Framework for Efficient E-Commerce Order Delivery," IIMA Working Papers WP 2020-11-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    9. María Isabel Arango & Edier Aristizábal & Federico Gómez, 2021. "Morphometrical analysis of torrential flows-prone catchments in tropical and mountainous terrain of the Colombian Andes by machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 983-1012, January.
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    11. Yu-Wen Chen & Yi-Chun Li & Chien-Yu Huang & Chia-Jung Lin & Chia-Jui Tien & Wen-Shiang Chen & Chia-Ling Chen & Keh-Chung Lin, 2023. "Predicting Arm Nonuse in Individuals with Good Arm Motor Function after Stroke Rehabilitation: A Machine Learning Study," IJERPH, MDPI, vol. 20(5), pages 1-12, February.
    12. Tommaso Colombo & Massimiliano Mangone & Andrea Bernetti & Marco Paoloni & Valter Santilli & Laura Palagi, 2019. "Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis," DIAG Technical Reports 2019-08, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    13. Wang, Weicheng & Chen, Jinglong & Zhang, Tianci & Liu, Zijun & Wang, Jun & Zhang, Xinwei & He, Shuilong, 2023. "An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    14. Xiaobo Yang & Zhilong Mi & Qingcai He & Binghui Guo & Zhiming Zheng, 2023. "Identification of Vital Genes for NSCLC Integrating Mutual Information and Synergy," Mathematics, MDPI, vol. 11(6), pages 1-15, March.

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