slice_quantile_mv_seq: Multivariate Quantile Slice Sampler from a sequence of...

In qslice: Implementations of Various Slice Samplers

Multivariate Quantile Slice Sampler from a sequence of conditional pseudo-targets

Description

Quantile slice sampler for a random vector (Heiner et al., 2026+). The pseudo-target is specified as a sequence of growing conditional distributions.

Usage

slice_quantile_mv_seq(x, log_target, pseudo_control, diagnostics = NULL)

Arguments

x	The current state (as a numeric vector).
log_target	A function taking numeric vector that evaluates the log-target density, returning a numeric scalar.
pseudo_control	A list with pseudo_init, a list output from pseudo_list describing the marginal pseudo-target for x[1]. Attributes of pseudo_init will be used in subsequent pseudo-targets, except for location and scale parameters. loc_fn, a function that specifies the location of a conditional pseudo-target given the elements in x that precede it. sc_fn, a function that specifies the scale of a conditional pseudo-target given the elements in x that precede it. lb, a numeric vector (same length as x) specifying the lower bound of support for each conditional pseudo-target. ub, a numeric vector (same length as x) specifying the upper bound of support for each conditional pseudo-target.
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 three elements: "x" is the new state, "u" is a vector of values of the sequence of conditional CDFs of the pseudo-targets associated with the returned value, and "nEvaluations" is the number of evaluations of the target function used to obtain the new state.

References

Heiner, M. J., Johnson, S. B., Christensen, J. R., and Dahl, D. B. (2026+), "Quantile Slice Sampling," arXiv preprint arXiv:2407.12608 \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.48550/arXiv.2407.12608")}

Examples

# Funnel distribution from Neal (2003).
K <- 10
n_iter <- 50 # MCMC iterations; set to 10e3 for more complete illustration
n <- 100 # number of iid samples from the target; set to 10e3 for more complete illustration
Y <- matrix(NA, nrow = n, ncol = K) # iid samples from the target
Y[, 1] <- rnorm(n, 0.0, 3.0)
for (i in 1:n) {
  Y[i, 2:K] <- rnorm(K - 1, 0.0, exp(0.5 * Y[i, 1]))
}
ltarget <- function(x) {
  dnorm(x[1], 0.0, 3.0, log = TRUE) +
    sum(dnorm(x[2:K], 0.0, exp(0.5 * x[1]), log = TRUE))
}
pseudo_control <- list(
  loc_fn = function(x) {
    0.0
  },
  sc_fn = function(x) {
    if (is.null(x)) {
      out <- 3.0
    } else {
      out <- exp(0.5 * x[1])
    }
    out
  },
  pseudo_init = pseudo_list(
    family = "t",
    params = list(loc = 0.0, sc = 3.0, df = 20),
    lb = -Inf,
    ub = Inf
  ),
  lb = rep(-Inf, K),
  ub = rep(Inf, K)
)
x0 <- runif(K)
draws <- matrix(rep(x0, n_iter + 1), nrow = n_iter + 1, byrow = TRUE)
draws_u <- matrix(rep(x0, n_iter), nrow = n_iter, byrow = TRUE)
n_eval <- 0
for (i in 2:(n_iter + 1)) {
  tmp <- slice_quantile_mv_seq(
    draws[i - 1, ],
    log_target = ltarget,
    pseudo_control = pseudo_control
  )
  draws[i, ] <- tmp$x
  draws_u[i - 1, ] <- tmp$u
  n_eval <- n_eval + tmp$nEvaluations
}
# (es <- coda::effectiveSize(coda::as.mcmc(draws)))
# mean(es)
n_eval / n_iter
sapply(1:K, function(k) {
  utility_shrinkslice(u = draws_u[, k], utility_type = "AUC")
})
hist(draws_u[, 1])
plot(draws[, 1], draws[, 2])
points(Y[, 1], Y[, 2], col = "blue", cex = 0.5)

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