mn4joint1condi: computes a joint distribution from a marginal and a...

In rbmn: Handling Linear Gaussian Bayesian Networks

computes a joint distribution from a marginal and a conditional one for multinormal distributions

Description

returns the expectation and variance of the multinormal normal distribution defined through a marginal subcomponent and a conditional distribution.

Usage

mn4joint1condi(lmar, lcon)

Arguments

lmar	list defining the distribution of the marginal part with elements mu, its expectation, and gamma, its variance matrix (in fact a /mn/ object).
lcon	list defining the distribution of the conditional part (see the Details section).

Details

The conditional distribution is defined with a list with elements a for the constant part of the expectation; b for the regression coefficient part of the expectation; and S for the residual variance matrix.

Value

A list with elements:

mu	The expectation vector.
gamma	The joint variance matrix.

that is a /mn/ object.

Examples

 lcon <- list(a=c(D=2, E=4),
 b=matrix(1:6, 2, dimnames=list(LETTERS[4:5],
 LETTERS[1:3])),
 S=matrix(c(1, 1, 1, 2), 2));

 print8mn(mn4joint1condi(rbmn0mn.01, lcon));

rbmn documentation built on July 9, 2023, 6:37 p.m.