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- W4295178746 abstract "The conditional mutual information I(X;Y|Z) measures the average information that X and Y contain about each other given Z. This is an important primitive in many learning problems including conditional independence testing, graphical model inference, causal strength estimation and time-series problems. In several applications, it is desirable to have a functional purely of the conditional distribution p_{Y|X,Z} rather than of the joint distribution p_{X,Y,Z}. We define the potential conditional mutual information as the conditional mutual information calculated with a modified joint distribution p_{Y|X,Z} q_{X,Z}, where q_{X,Z} is a potential distribution, fixed airport. We develop K nearest neighbor based estimators for this functional, employing importance sampling, and a coupling trick, and prove the finite k consistency of such an estimator. We demonstrate that the estimator has excellent practical performance and show an application in dynamical system inference." @default.
- W4295178746 created "2022-09-11" @default.
- W4295178746 creator A5055459719 @default.
- W4295178746 creator A5060138971 @default.
- W4295178746 date "2017-10-13" @default.
- W4295178746 modified "2023-09-30" @default.
- W4295178746 title "Potential Conditional Mutual Information: Estimators, Properties and Applications" @default.
- W4295178746 doi "https://doi.org/10.48550/arxiv.1710.05012" @default.
- W4295178746 hasPublicationYear "2017" @default.
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