Covariance mapping: Difference between revisions

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In [[statistics]], '''covariance mapping''' is an extension of the [[covariance]] concept from [[random variables]] to [[random function]]s. Normal covariance is a scalar (a single number) that measures statistical relation between two random variables. Covariance maps are matrices (arrays of numbers) that show statistical relations between different regions of random functions. Statistically independent regions of the functions show up on the map as zero-level flatland, while positive or negative correlations show up, respectively, as hills or valleys.