Generates random matrices, distributed according to the Wishart distribution with parameters \(b\) and \(D\), \(W(b, D)\).
rwish( n = 1, p = 2, b = 3, D = diag( p ) )
The number of samples required.
The number of variables (nodes).
The degree of freedom for Wishart distribution, \(W(b, D)\).
The positive definite \((p \times p)\) "scale" matrix for Wishart distribution, \(W(b, D)\). The default is an identity matrix.
A numeric array, say A, of dimension \((p \times p \times n)\), where each \(A[,,i]\) is a positive definite matrix, a realization of the Wishart distribution \(W(b, D)\). Note, for the case \(n=1\), the output is a matrix.
Sampling from Wishart distribution, \(K \sim W(b, D)\), with density:
$$Pr(K) \propto |K| ^ {(b - 2) / 2} \exp \left\{- \frac{1}{2} \mbox{trace}(K \times D)\right\},$$
which \(b > 2\) is the degree of freedom and \(D\) is a symmetric positive definite matrix.
Lenkoski, A. (2013). A direct sampler for G-Wishart variates, Stat, 2:119-128
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R
Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):1-30
# NOT RUN {
sample <- rwish( n = 3, p = 5, b = 3, D = diag( 5 ) )
round( sample, 2 )
# }
Run the code above in your browser using DataLab