Kernel estimate for (Conditional) Mutual Information 1.0

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Mutual information I(X,Y) measures the degree of dependence (in terms of probability theory) between two random variables X and Y. Is is non-negative and equal to zero when X and Y are mutually independent. Conditional mutual information I(X,Y|Z) is the expected value of I(X,Y) given the value of Z.Data is first copula-transformed, then marginal and joint probability distributions are estimated using Gaussian kernels.Useful in construction and verification of gene regulatory networks (see e.g. http://www.biomedcentral.com/1471-2105/7/S1/S7) given gene expression data. This quantity is robust and can trace non-linear dependencies and indirect interactions in data.

Requirements: No special requirements

Platforms: Matlab

Keyword: Construction, Distributions, Estimated, Gaussian, Joint, Kernels, Marginal, Verification

Users rating: 0/10

License: Shareware

Size: 10 KB

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