A covariance matrix generalizes the idea of variance to multiple dimensions, where the i-th j-th element in the covariance matrix is the covariance between the i-th and j-th random variables. Covariance matrices are common throughout both statistics and machine learning and often arise when dealing with multivariate distributions.
This concept has the prerequisites:
- Understand how to calculate the entries of a covariance matrix
- Understand the difference between positive and negative covariances
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Supplemental resources (the following are optional, but you may find them useful)
→ The Analysis Factor
Location: Article: Covariance Matrix
- The multivariate Gaussian is a widely used distribution parameterized in terms of a mean vector and covariance matrix.
- The Cauchy-Schwartz inequality for covariance follows from the fact that covariance matrices are PSD.
- Principal component analysis is a data analysis method applied to the covariance matrix.
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