smcPotts

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

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.

Aliases
  • smcPotts
Documentation reproduced from package bayesImageS, version 0.4-0, License: GPL (>= 2)

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