Inverse-Wishart: The Inverse Wishart Distribution

In nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling

The Inverse Wishart Distribution

Description

Density and random generation for the Inverse Wishart distribution, using the Cholesky factor of either the scale matrix or the rate matrix.

Usage

dinvwish_chol(x, cholesky, df, scale_param = TRUE, log = FALSE)

rinvwish_chol(n = 1, cholesky, df, scale_param = TRUE)

Arguments

x	vector of values.
cholesky	upper-triangular Cholesky factor of either the scale matrix (when scale_param is TRUE) or rate matrix (otherwise).
df	degrees of freedom.
scale_param	logical; if TRUE the Cholesky factor is that of the scale matrix; otherwise, of the rate 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 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

dinvwish_chol gives the density and rinvwish_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

df <- 40
ch <- chol(matrix(c(1, .7, .7, 1), 2))
x <- rwish_chol(1, ch, df = df)
dwish_chol(x, ch, df = df)

nimble documentation built on Sept. 11, 2024, 7:10 p.m.