MultivariateNormal: The Multivariate Normal Distribution

MultivariateNormalR Documentation

The Multivariate Normal Distribution

Description

Density and random generation for the multivariate normal distribution, using the Cholesky factor of either the precision matrix or the covariance matrix.

Usage

dmnorm_chol(x, mean, cholesky, prec_param = TRUE, log = FALSE)

rmnorm_chol(n = 1, mean, cholesky, prec_param = TRUE)

Arguments

x

vector of values.

mean

vector of values giving the mean of the distribution.

cholesky

upper-triangular Cholesky factor of either the precision matrix (when prec_param is TRUE) or covariance matrix (otherwise).

prec_param

logical; if TRUE the Cholesky factor is that of the precision matrix; otherwise, of the covariance matrix.

log

logical; if TRUE, probability density is returned on the log scale.

n

number of observations (only n=1 is handled currently).

Details

See Gelman et al., Appendix A or the BUGS manual for mathematical details. The rate matrix as used here is defined as the inverse of the scale matrix, S^{-1}, given in Gelman et al.

Value

dmnorm_chol gives the density and rmnorm_chol generates random deviates.

Author(s)

Christopher Paciorek

References

Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2004) Bayesian Data Analysis, 2nd ed. Chapman and Hall/CRC.

See Also

Distributions for other standard distributions

Examples

mean <- c(-10, 0, 10)
covmat <- matrix(c(1, .9, .3, .9, 1, -0.1, .3, -0.1, 1), 3)
ch <- chol(covmat)
x <- rmnorm_chol(1, mean, ch, prec_param = FALSE)
dmnorm_chol(x, mean, ch, prec_param = FALSE)

nimble documentation built on March 18, 2022, 8:03 p.m.