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Graphical models can still be used when the variables of choice are continuous. In these cases, the probability distribution is represented as a [[multivariate probability distribution]] over continuous variables. Each family of distribution will then impose certain properties on the graphical model. [[Multivariate Gaussian distribution]] is one of the most convenient distributions in this problem. The simple form of the probability, and the direct relation with the corresponding graphical model makes it a popular choice among researchers.
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Gaussian graphical models are multivariate probability distributions encoding a network of dependencies among variables. Let <math>\Theta=[\theta_1, \theta_2, \dots, \theta_n]</math> be a set of <math>n</math> variables, such as <math>n</math> [[dihedral angles]], and let <math>f(\Theta=D)</math> be the value of the [[probability density function]] at a particular value ''D''. A multivariate Gaussian graphical model defines this probability as follows:
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