Last data update: 2014.03.03
R: MCMC samplers for Gibbs Random Fields
sampler.mrf R Documentation
MCMC samplers for Gibbs Random Fields
Description
sampler.mrf gives approximate sample from the likelihood of a general Potts model defined on a rectangular h x w lattice (h ≤ w ) with either a first order or a second order dependency structure. Available options are the Gibbs sampler (Geman and Geman (1984)) and the Swendsen-Wang algorithm (Swendsen and Wang (1987)).
Usage
sampler.mrf(iter, sampler = "Gibbs" , h, w,
param, ncolors = 2, nei = 4, pot = NULL,
top = NULL, left = NULL, bottom = NULL, right = NULL,
corner = NULL, initialise = TRUE, random = TRUE, view = FALSE)
Arguments
iter
Number of iterations of the algorithm.
sampler
The method to be used. The latter must be one of "Gibbs" or "SW" corresponding respectively to the Gibbs sampler and the Swendsen-Wang algorithm.
h
the number of rows of the rectangular lattice.
w
the number of columns of the rectangular lattice.
param
numeric entry setting the interaction parameter (edges parameter)
ncolors
the number of states for the discrete random variables. By default, ncolors = 2.
nei
the number of neighbors. The latter must be one of nei = 4 or nei = 8, which respectively correspond to a first order and a second order dependency structure. By default, nei = 4.
pot
numeric entry setting homogeneous potential on singletons (vertices parameter). By default, pot = NULL
top, left, bottom, right, corner
numeric entry setting constant borders for the lattice. By default, top = NULL, left = NULL, bottom = NULL, right = NULL, corner = NULL.
initialise
Logical value indicating whether initial guess should be randomly drawn.
random
Logical value indicating whether the sites should be updated sequentially or randomdly. Used only with the "Gibbs" option.
view
Logical value indicating whether the draw should be printed. Do not display the optional borders.
References
Geman, S. and Geman, D. (1984). Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intellignence , 6(6) :721-741.
Swendsen, R. H. and Wang, J.-S. (1987). Nonuniversal critical dynamics in Monte Carlo simulations. Pysical Review Letters , 58(2) :86-88.
See Also
The “GiRaF-introduction” vignette
Examples
# Algorithm settings
n <- 200
method <- "Gibbs"
# Dimension of the lattice
height <- width <- 100
# Interaction parameter
Beta <- 0.6 # Isotropic configuration
# Beta <- c(0.6, 0.6) # Anisotropic configuration when nei = 4
# Beta <- c(0.6, 0.6, 0.6, 0.6) # Anisotropic configuration when nei = 8
# Number of colors
K <- 2
# Number of neighbors
G <- 4
# Optional potential on sites
potential <- runif(K,-1,1)
# Optional borders.
Top <- Bottom <- sample(0:(K-1), width, replace = TRUE)
Left <- Right <- sample(0:(K-1), height, replace = TRUE)
Corner <- sample(0:(K-1), 4, replace = TRUE)
# Sampling method for the default setting
sampler.mrf(iter = n, sampler = method, h = height, w = width,
param = Beta, view = TRUE)
# Sampling using an existing configuration as starting point
sampler.mrf(iter = n, sampler = method, h = height, w = width,
ncolors = K, nei = G, param = Beta,
initialise = FALSE, view = TRUE)
# Specifying optional arguments. The users may omit to mention all
# the non-existing borders
sampler.mrf(iter = n, sampler = method, h = height, w = width,
ncolors = K, nei = G, param = Beta,
pot = potential, top = Top, left = Left, bottom = Bottom,
right = Right, corner = Corner, view = TRUE)
# Gibbs sampler with sequential updates of the sites.
sampler.mrf(iter = n, sampler = "Gibbs", h = height, w = width,
ncolors = K, nei = G, param = Beta,
random = FALSE, view = TRUE)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(GiRaF)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GiRaF/sampler.mrf.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sampler.mrf
> ### Title: MCMC samplers for Gibbs Random Fields
> ### Aliases: sampler.mrf
>
> ### ** Examples
>
> # Algorithm settings
> n <- 200
> method <- "Gibbs"
>
> # Dimension of the lattice
> height <- width <- 100
>
> # Interaction parameter
> Beta <- 0.6 # Isotropic configuration
> # Beta <- c(0.6, 0.6) # Anisotropic configuration when nei = 4
> # Beta <- c(0.6, 0.6, 0.6, 0.6) # Anisotropic configuration when nei = 8
>
> # Number of colors
> K <- 2
> # Number of neighbors
> G <- 4
>
> # Optional potential on sites
> potential <- runif(K,-1,1)
> # Optional borders.
> Top <- Bottom <- sample(0:(K-1), width, replace = TRUE)
> Left <- Right <- sample(0:(K-1), height, replace = TRUE)
> Corner <- sample(0:(K-1), 4, replace = TRUE)
>
> # Sampling method for the default setting
> sampler.mrf(iter = n, sampler = method, h = height, w = width,
+ param = Beta, view = TRUE)
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[42,] 0 1 1 1 0 1 1 0 0 0 0 0
[43,] 0 0 0 0 0 1 1 1 0 0 0 0
[44,] 0 0 0 1 0 1 1 1 1 1 0 0
[45,] 1 1 1 1 1 1 1 1 1 1 0 0
[46,] 1 0 1 1 1 1 1 1 1 1 0 0
[47,] 1 1 1 1 1 1 1 1 0 0 1 1
[48,] 1 1 0 1 1 1 0 0 0 1 0 0
[49,] 1 1 1 1 1 0 0 0 0 1 1 1
[50,] 0 1 1 1 1 1 1 1 1 1 1 0
[51,] 0 1 1 1 1 0 0 0 0 1 1 1
[52,] 0 0 0 0 0 0 0 0 0 1 1 1
[53,] 1 0 0 0 0 1 0 1 1 1 1 1
[54,] 1 1 0 0 0 1 0 1 1 1 1 1
[55,] 1 1 1 1 0 0 0 1 1 1 1 1
[56,] 0 0 1 1 1 0 1 1 1 1 1 0
[57,] 0 0 1 1 0 1 0 1 1 1 1 1
[58,] 0 0 0 0 1 0 0 1 1 1 1 1
[59,] 1 1