1. The multivariate normal distribution is a commonly encountered multivariate distribution. 2. Hence the multivariate normal distribution is an example of the class of elliptical distributions. 3. This distribution arises in multivariate statistics as a derivative of the multivariate normal distribution . 4. Then, the multivariate normal distribution can be equivalently represented as a moment matrix: 5. Gaussian processes can be seen as an infinite-dimensional generalization of multivariate normal distributions . 6. These semantics render the partial sweeping operation a useful method for manipulating multivariate normal distributions . 7. The multivariate stable distribution can also be thought as an extension of the multivariate normal distribution . 8. A random vector is said to have the multivariate normal distribution if it satisfies the following equivalent conditions. 9. The Fisher information matrix for estimating the parameters of a multivariate normal distribution has a closed form expression. 10. For large sample sizes, the central limit theorem says this distribution tends toward a certain multivariate normal distribution .
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