Generate n random matrices, distributed according
to the inverse Wishart distribution with parameters Sigma
and
df
,
Note there are different ways of parameterizing the Inverse
Wishart distribution, so check which one you need.
Here, if
rInvWishart(n, df, Sigma)
a numeric array, say R
, of dimension
R[,,i]
is a realization of the inverse Wishart distribution
rWishart
function.
integer sample size.
numeric parameter, "degrees of freedom".
positive definite
Dawid, A. (1981). Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application. Biometrika, 68(1), 265-274. tools:::Rd_expr_doi("10.2307/2335827")
Gupta, A. K. and D. K. Nagar (1999). Matrix variate distributions. Chapman and Hall.
Mardia, K. V., J. T. Kent, and J. M. Bibby (1979) Multivariate Analysis, London: Academic Press.
rWishart
, rCholWishart
,
and rInvCholWishart
set.seed(20180221)
A <- rInvWishart(1L, 10, 5 * diag(5L))[, , 1]
set.seed(20180221)
B <- stats::rWishart(1L, 10, .2 * diag(5L))[, , 1]
A %*% B
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