mcmcPottsNoData {bayesImageS} | R Documentation |
Simulate pixel labels using chequerboard Gibbs sampling.
mcmcPottsNoData(beta, k, neighbors, blocks, niter = 1000, random = TRUE)
beta |
The inverse temperature parameter of the Potts model. |
k |
The number of unique labels. |
neighbors |
A matrix of all neighbors in the lattice, one row per pixel. |
blocks |
A list of pixel indices, dividing the lattice into independent blocks. |
niter |
The number of iterations of the algorithm to perform. |
random |
Whether to initialize the labels using random or deterministic starting values. |
A list containing the following elements:
alloc
An n by k matrix containing the number of times that pixel i was allocated to label j.
z
An (n+1)
by k matrix containing the final sample from the Potts model after niter iterations of chequerboard Gibbs.
sum
An niter
by 1 matrix containing the sum of like neighbors, i.e. the sufficient statistic of the Potts model, at each iteration.
# Swendsen-Wang for a 2x2 lattice neigh <- matrix(c(5,2,5,3, 1,5,5,4, 5,4,1,5, 3,5,2,5), nrow=4, ncol=4, byrow=TRUE) blocks <- list(c(1,4), c(2,3)) res.Gibbs <- mcmcPottsNoData(0.7, 3, neigh, blocks, niter=200) res.Gibbs$z res.Gibbs$sum[200]