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