make.gaussian: Gaussian distribution objects
In SamplerCompare: A Framework for Comparing the Performance of MCMC Samplers

Description Usage Arguments Details See Also Examples

Gaussian distribution objects

make.gaussian(mean, sigma=NULL, rho=NULL)
N2weakcor.dist
N4poscor.dist
N4negcor.dist

`mean`	The mean of the distribution as a numeric vector; implicitly specifies the dimension.
`sigma`	The covariance of the distribution.
`rho`	The marginal correlations between parameters.

make.gaussian returns a distribution object representing a multivariate normal distribution. If sigma is specified, that is taken to be its covariance. Otherwise, if rho is specified, the covariance is taken to be a matrix with ones on the diagonal and rho on the off-diagonal elements. To preserve positive definiteness, rho must be between -1/(length(mean)-1) and 1.

N2weakcor.dist, N4poscor.dist, and N4negcor.dist are predefined distributions generated with make.gaussian. They are intended to be used as test cases with compare.samplers. The examples below show how they are defined. N2weakcor.dist is a weakly positively correlated two-dimensional Gaussian. N4poscor.dist is a highly positively correlated four-dimensional Gaussian. N4negcor.dist is a highly negatively correlated four-dimensional Gaussian. N4poscor.dist and N4negcor.dist are similarly conditioned, but N4poscor.dist has one large eigenvalue and three small ones, while N4negcor.dist has one small eigenvalue and three large ones.

compare.samplers, make.dist

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  N2weakcor.dist <- make.gaussian(c(0,0), rho=0.8)
  N4poscor.dist <- make.gaussian(c(1,2,3,4), rho=0.999)
  N4negcor.dist <- make.gaussian(c(1,2,3,4), rho=-0.3329)