Sample the latent field [S | Y] (Poisson, non-nested) via C++ MALA

Draws posterior samples of the latent Gaussian field for the Poisson, non-nested case. The Laplace mode (Newton step) and the adaptive Metropolis- adjusted Langevin (MALA) loop both run in C++ for speed, with a fixed-seed path for reproducibility.

Usage

laplace_sampling(mu, Sigma, y, units.m, control.mcmc)

Arguments

mu

prior mean vector.

Sigma

prior covariance matrix.

y

count vector.

units.m

offset vector.

control.mcmc

list from control_mcmc.

Value

list with samples (kept x n matrix) and h (step sizes).

Examples

# \donttest{
## sample [S | Y] for a tiny 10-unit Poisson example
set.seed(1)
n <- 10
D <- as.matrix(dist(cbind(runif(n), runif(n))))
Sigma <- 0.4 * exp(-D / 0.3)
mu <- rep(log(2), n); m <- rep(100, n)
y <- rpois(n, m * exp(mu + as.numeric(t(chol(Sigma)) %*% rnorm(n))))
out <- laplace_sampling(mu, Sigma, y, m, control_mcmc(n.sim = 2000, burnin = 500, thin = 3))
dim(out$samples)     # (retained draws) x n
#> [1] 500  10
# }