Multivariate Gaussian

Resources:

• page=41

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Given by its expectation vector $\mu$ and symmetric positive definite covariance matrix $P$, according to

$$\mathcal{N}(x;\mu ,P)=\frac{1}{(2\pi {)}^{n/2}|P{|}^{1/2}}\exp \left(-\frac{1}{2}(x-\mu {)}^T{P}^{-1}(x-\mu )\right)$$

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Properties

Independence

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Linearity

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Marginalization and Conditioning

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