nimble: Inverse-Wishart – R documentation

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Inverse-Wishart

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.

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

MCMC, Particle Filtering, and Programmable Hierarchical Modeling

v0.11.0

BSD_3_clause + file LICENSE | GPL (>= 2)

Authors

Perry de Valpine [aut], Christopher Paciorek [aut, cre], Daniel Turek [aut], Nick Michaud [aut], Cliff Anderson-Bergman [aut], Fritz Obermeyer [aut], Claudia Wehrhahn Cortes [aut] (Bayesian nonparametrics system), Abel Rodrìguez [aut] (Bayesian nonparametrics system), Duncan Temple Lang [aut] (packaging configuration), Sally Paganin [aut] (reversible jump MCMC), Jagadish Babu [ctb] (code for the compilation system for an early version of NIMBLE), Lauren Ponisio [ctb] (contributions to the cross-validation code), Peter Sujan [ctb] (multivariate t distribution code)

Initial release

2021-04-16