Gaussian distribution objects

1 2 3 4 |

`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`

1 2 3 | ```
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)
``` |

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