Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?
Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur Graph Search.
In probability and statistics, a compound probability distribution (also known as a mixture distribution or contagious distribution) is the probability distribution that results from assuming that a random variable is distributed according to some parametrized distribution, with (some of) the parameters of that distribution themselves being random variables. If the parameter is a scale parameter, the resulting mixture is also called a scale mixture. The compound distribution ("unconditional distribution") is the result of marginalizing (integrating) over the latent random variable(s) representing the parameter(s) of the parametrized distribution ("conditional distribution"). A compound probability distribution is the probability distribution that results from assuming that a random variable is distributed according to some parametrized distribution with an unknown parameter that is again distributed according to some other distribution . The resulting distribution is said to be the distribution that results from compounding with . The parameter's distribution is also called the mixing distribution or latent distribution. Technically, the unconditional distribution results from marginalizing over , i.e., from integrating out the unknown parameter(s) . Its probability density function is given by: The same formula applies analogously if some or all of the variables are vectors. From the above formula, one can see that a compound distribution essentially is a special case of a marginal distribution: The joint distribution of and is given by and the compound results as its marginal distribution: If the domain of is discrete, then the distribution is again a special case of a mixture distribution. The compound distribution will depend on the specific expression of each distribution, as well as which parameter of is distributed according to the distribution , and the parameters of will include any parameters of that are not marginalized, or integrated, out.
,
,