dmgbayes.gaussian.marginal.bidirected(dmgbayes)R Documentation

Marginal log-likelihood of a zero-mean Gaussian models with marginal independence constraints

Description

Returns the marginal log-likelihood of a zero-mean Gaussian model where the covariance matrix is constrained to have some zero covariance entries.

Usage

dmgbayes.gaussian.marginal.bidirected(var.names, bidirected, train,
                                      prior.V, dmg.df, mc.num.samples, mc.options)

Arguments

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

Author(s)

Ricardo Silva, Statistical Laboratory, University of Cambridge

References

Silva, R. (2008). "dmgBayes: Software for Bayesian Inference in Mixed Graph Models and Structural Equation Models"

See Also

dmgbayes.gaussiansample


[Package dmgbayes version 1.0 Index]