# mn4joint1condi: computes a joint distribution from a marginal and a... In rbmn: Handling Linear Gaussian Bayesian Networks

## Description

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

## Usage

 `1` ```mn4joint1condi(lmar, lcon) ```

## Arguments

 `lmar` list defining the distribution of the marginal part with `\$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 having `\$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:

 `\\$mu` The expectation vector. `\\$gamma` The joint variance matrix.

that is a /mn/ object.

## Examples

 ```1 2 3 4 5 6``` ``` 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 Jan. 16, 2021, 5:31 p.m.