gibbsPotts {bayesImageS} | R Documentation |

## Fit a hidden Potts model to the observed data, using a fixed value of beta.

### Description

Fit a hidden Potts model to the observed data, using a fixed value of beta.

### Usage

```
gibbsPotts(y, labels, beta, mu, sd, neighbors, blocks, priors, niter = 1)
```

### Arguments

`y` |
A vector of observed pixel data. |

`labels` |
A matrix of pixel labels. |

`beta` |
The inverse temperature parameter of the Potts model. |

`mu` |
A vector of means for the mixture components. |

`sd` |
A vector of standard deviations for the mixture components. |

`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. |

`priors` |
A list of priors for the parameters of the model. |

`niter` |
The number of iterations of the algorithm to perform. |

### Value

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

[Package

*bayesImageS*version 0.6-1 Index]