gnorm {BDgraph} | R Documentation |
Calculates log of the normalizing constant of G-Wishart distribution based on the Monte Carlo method, developed by Atay-Kayis and Massam (2005).
gnorm( adj, b = 3, D = diag( ncol( adj ) ), iter = 100 )
adj |
The adjacency matrix corresponding to the graph structure. It is an upper triangular matrix in which aij = 1 if there is a link between notes i and j, otherwise aij = 0. |
b |
The degree of freedom for G-Wishart distribution, W_G(b, D). |
D |
The positive definite (p \times p) "scale" matrix for G-Wishart distribution, W_G(b,D). The default is an identity matrix. |
iter |
The number of iteration for the Monte Carlo approximation. |
Log of the normalizing constant approximation using Monte Carlo method for a G-Wishart distribution, K \sim W_G(b, D), with density:
Pr(K) = \frac{1}{I(b, D)} |K| ^ {(b - 2) / 2} \exp ≤ft\{- \frac{1}{2} \mbox{trace}(K \times D)\right\}.
Log of the normalizing constant of G-Wishart distribution.
Reza Mohammadi a.mohammadi@uva.nl
Atay-Kayis, A. and Massam, H. (2005). A monte carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models, Biometrika, 92(2):317-335
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Uhler, C., et al (2018) Exact formulas for the normalizing constants of Wishart distributions for graphical models, The Annals of Statistics 46(1):90-118
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
## Not run: # adj: adjacency matrix of graph with 3 nodes and 2 links adj <- matrix( c( 0, 0, 1, 0, 0, 1, 0, 0, 0 ), 3, 3, byrow = TRUE ) gnorm( adj, b = 3, D = diag( 3 ) ) ## End(Not run)