slice_latent: Latent Slice Sampler

In qslice: Implementations of Various Slice Samplers

Latent Slice Sampler

Description

Single update using the latent slice sampler of Li and Walker (2023).

Usage

slice_latent(x, s, log_target, rate)

Arguments

x	The current state (as a numeric scalar).
s	A random variable that determines the length of the initial shrinking interval.
log_target	A function taking numeric scalar that evaluates the (potentially unnormalized) log-target density, returning a numeric scalar.
rate	The rate parameter for the distribution of s.

Value

A list containing three elements:

x is the new state of the target variable.

s is the new state of the latent scale variable.

nEvaluations is the number of evaluations of the target function used to obtain the new state.

References

Li, Y. and Walker, S. G. (2023), "A latent slice sampling algorithm," Computational Statistics and Data Analysis, 179, 107652. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.csda.2022.107652")}

Examples

lf <- function(x) dbeta(x, 3, 4, log = TRUE)
draws <- numeric(10) # set to numeric(1e3) for more complete illustration
nEvaluations <- 0L
s <- 0.5
for (i in seq.int(2, length(draws))) {
  out <- slice_latent(draws[i - 1], s, log_target = lf, rate = 0.3)
  draws[i] <- out$x
  s <- out$s
  nEvaluations <- nEvaluations + out$nEvaluations
}
nEvaluations / (length(draws) - 1)
plot(density(draws), xlim = c(0, 1))
curve(exp(lf(x)), 0, 1, col = "blue", add = TRUE)

qslice documentation built on June 22, 2024, 10:49 a.m.