rcondmvtnorm: Conditional samples of Gaussian or Student subvectors

In lbelzile/mgp: Multivariate generalized Pareto models

Conditional samples of Gaussian or Student subvectors

Description

The function samples (truncated) Gaussian or Student vectors corresponding to indices ind given the values at the remaining index. The location vector mu and the scale matrix sigma are those of the d+p vector. The routine relies on the CDF approximation based on minimax exponential tilting implemented in the TruncatedNormal package.

Usage

rcondmvtnorm(n = 1L, ind, x, lbound, ubound, mu, sigma, df = NULL,
  model = c("norm", "stud"))

Arguments

n	sample size for the random vector; default to 1.
ind	a d vector of indices to impute with integer entries in \{1, \ldots, d+p\}
x	a p vector with the values of process at remaining coordinates
lbound	d vector of lower bounds
ubound	d vector of upper bounds for truncated
mu	d+p vector of location parameters
sigma	d+p by d+p scale matrix
df	degrees of freedom of the d+p dimensional Student process
model	string indicating family, either norm for Gaussian or stud for Student-t

Value

an n by d matrix of conditional simulations

lbelzile/mgp documentation built on Aug. 5, 2023, 2:34 a.m.