Open AccessТotal probability formula for vector Gaussian distributions
Author(s) -
В. С. Муха,
Н. Ф. Како
Publication year - 2021
Publication title -
doklady belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki
Language(s) - English
Resource type - Journals
eISSN - 2708-0382
pISSN - 1729-7648
DOI - 10.35596/1729-7648-2021-19-2-58-64
Subject(s) - mathematics , conditional probability , probability density function , regular conditional probability , conditional expectation , conditional probability distribution , statistical distance , probability theory , chain rule (probability) , probability distribution , marginal distribution , convolution of probability distributions , multivariate random variable , random variable , moment generating function , probability mass function , statistics
The total probability formula for continuous random variables is the integral of product of two probability density functions that defines the unconditional probability density function from the conditional one. The need for calculation of such integrals arises in many applications, for instant, in statistical decision theory. The statistical decision theory attracts attention due to the ability to formulate the problems in a strict mathematical form. One of the technical problems solved by the statistical decision theory is the problem of dual control that requires calculation of integrals connected with the multivariate probability distributions. The necessary integrals are not available in the literature. One theorem on the total probability formula for vector Gaussian distributions was published by the authors earlier. In this paper we repeat this theorem and prove a new theorem that uses more familiar form of the initial data and has more familiar form of the result. The new form of the theorem allows us to obtain the unconditional mathematical expectation and the unconditional variance-covariance matrix very simply. We also confirm the new theorem by direct calculation for the case of the simple linear regression.