Standard parametric modelsΒΆ

Parametric models aim to describe probability distributions of a random variable with the aid of a limited number of parameters \vect{\theta}. Therefore, in the case of continuous variables (i.e. where all possible values are continuous), this means that the probability density of \vect{X} = \left( X^1,\ldots,X^{n_X} \right) can be expressed as f_X(\vect{x};\vect{\theta}). In the case of discrete variables (i.e. those which take only discrete values), their probabilities can be described in the form \Prob{\vect{X} = \vect{x};\vect{\theta}}.