# mcmcPotts

From bayesImageS v0.4-0
by Matt Moores

##### Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.

Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.

##### Usage

```
mcmcPotts(y, neighbors, blocks, slices, niter = 55000, nburn = 5000,
priors = NULL, mh = list(algorithm = "pseudolikelihood", bandwidth = 0.2),
truth = 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.

- slices
Deprecated.

- niter
The number of iterations of the algorithm to perform.

- nburn
The number of iterations to discard as burn-in.

- priors
A list of priors for the parameters of the model.

- mh
A list of options for the Metropolis-Hastings algorithm.

- truth
A matrix containing the ground truth for the pixel labels.

##### Value

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

##### See Also

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

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