mrfbayes: Metropolis-Hasting algorithm for Markov Random Fields on...

In Freguglia/mrf2dbayes: Bayesian Analysis of Markov Random Fields on 2d Lattices

Metropolis-Hasting algorithm for Markov Random Fields on lattices

Description

Metropolis-Hasting algorithm for Markov Random Fields on lattices

Usage

mrfbayes(
  z,
  llapprox,
  nsamples = 1000,
  init_theta = "zero",
  sdprior = 10,
  sdkernel = 0.05,
  verbose = interactive()
)

Arguments

z	The observed random field.
llapprox	The likelihood approximation to be used.
nsamples	Number of MCMC samples.
init_theta	Initial values of the MCMC algorithm. Set to "zero" to automatically create a vector equal to zero with appropriate length or "pl" to use the Maximum Pseudolikelihood estimator of z as the initial value.
sdprior	Sample Deviation of the Normal distributions used as prior.
sdkernel	Sample Deviation of the Normal distributions of the transition kernel.
verbose	logical value indicating wheter the algorithm progress should be printed.

Value

An object of class mrfbayes_out

Freguglia/mrf2dbayes documentation built on Jan. 19, 2024, 11:21 p.m.