smcPotts: Fit the hidden Potts model using approximate Bayesian...

In bayesImageS: Bayesian Methods for Image Segmentation using a Potts Model

Fit the hidden Potts model using approximate Bayesian computation with sequential Monte Carlo (ABC-SMC).

Description

Fit the hidden Potts model using approximate Bayesian computation with sequential Monte Carlo (ABC-SMC).

Usage

smcPotts(
  y,
  neighbors,
  blocks,
  param = list(npart = 10000, nstat = 50),
  priors = NULL
)

Arguments

y	A vector of observed pixel data.
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.
param	A list of options for the ABC-SMC algorithm.
priors	A list of priors for the parameters of the model.

Value

A matrix containing SMC samples for the parameters of the Potts model.

bayesImageS documentation built on Nov. 6, 2025, 1:18 a.m.