slice_genelliptical_mv: Generalized Elliptical Slice Sampler (Multivariate)

In qslice: Implementations of Various Slice Samplers

Generalized Elliptical Slice Sampler (Multivariate)

Description

Generalized Elliptical Slice Sampler, Algorithm 2 of Nishihara et al. (2014)

Usage

slice_genelliptical_mv(
  x,
  log_target,
  mu,
  Sig,
  df,
  is_chol = FALSE,
  diagnostics = NULL
)

Arguments

x	The current state (as a numeric vector).
log_target	A function taking numeric vector that evaluates the natural logarithm of the unnormalized target density. Returns a numeric scalar.
mu	A numeric vector with the location parameter of the Student t pseudo-target.
Sig	Positive definite scale matrix of the Student t pseudo-target. Alternatively, a lower-triangular matrix with the Cholesky factor of the scale matrix (for faster computation).
df	Degrees of freedom of Student t pseudo-target.
is_chol	Logical, is the supplied Sig in Cholesky (lower triangular) format? Default is false.
diagnostics	Logical, indicating whether the sampler should be run in diagnostic mode for informative error handling. When diagnostics is NULL, it is reset to match the value of getOption("qslice.diagnostics") which defaults to FALSE if the option is not set. Default for diagnostics is NULL.

Value

A list containing two elements: x is the new state and nEvaluations is the number of evaluations of the target function used to obtain the new state.

References

Nishihara, R., Murray, I., and Adams, R. P. (2014), "Parallel MCMC with Generalized Elliptical Slice Sampling," Journal of Machine Learning Research, 15, 2087-2112. https://jmlr.org/papers/v15/nishihara14a.html

Examples

lf <- function(x) dbeta(x[1], 3, 4, log = TRUE) + dbeta(x[2], 5, 3, log = TRUE)
n_iter <- 10 # set to 1e4 for more complete illustration
draws <- matrix(0.3, nrow = n_iter, ncol = 2)
nEvaluations <- 0L
for (i in seq.int(2, n_iter)) {
  out <- slice_genelliptical_mv(draws[i - 1,], log_target = lf,
              mu = c(0.5, 0.5), Sig = matrix(c(0.5, 0.25, 0.25, 0.5), nrow = 2),
              df = 5)
  draws[i,] <- out$x
  nEvaluations <- nEvaluations + out$nEvaluations
}
nEvaluations / (n_iter - 1)
plot(draws[,1], draws[,2], xlim = c(0, 1))
hist(draws[,1], freq = FALSE); curve(dbeta(x, 3, 4), col = "blue", add = TRUE)
hist(draws[,2], freq = FALSE); curve(dbeta(x, 5, 3), col = "blue", add = TRUE)

qslice documentation built on Oct. 22, 2026, 1:06 a.m.