dmgbayes.gaussian.marginal.bidirected(dmgbayes) | R Documentation |
Returns the marginal log-likelihood of a zero-mean Gaussian model where the covariance matrix is constrained to have some zero covariance entries.
dmgbayes.gaussian.marginal.bidirected(var.names, bidirected, train, prior.V, dmg.df, mc.num.samples, mc.options)
var.names |
Array of string naming all variables |
bidirected |
A m' x 2 array, m' being the total number of bi-directed edges. Each two-dimensional row (i, j) encodes a bi-directed edge connecting the ith and jth variables |
train |
Data which we condition on when computing the posterior |
prior.V |
A p x p matrix with the matrix parameter of the G-Inverse Wishart prior for the covariance matrix of the error terms |
dmg.df |
Degrees of freedom of the same prior |
mc.num.samples |
Total number of samples to be generated in the Markov chain |
mc.options |
A numeric array contain miscellaneous options: mc.options[1] is a number between 1 and 4 indicating verbosity level of output messages returned to the R console |
Ricardo Silva, Statistical Laboratory, University of Cambridge
Silva, R. (2008). "dmgBayes: Software for Bayesian Inference in Mixed Graph Models and Structural Equation Models"