# smcPotts

From bayesImageS v0.4-0
by Matt Moores

##### 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.

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

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