R: Conjugate Prior for Gaussian Mixtures.

priorControl {mclust} | R Documentation

## Conjugate Prior for Gaussian Mixtures.

### Description

Specify a conjugate prior for Gaussian mixtures.

### Usage

    priorControl(functionName = "defaultPrior", ...)

### Arguments

`functionName` | The name of the function specifying the conjugate prior. By default the function `defaultPrior` is used, and this can also be used as a template for alternative specification.
`...` | Optional named arguments to the function specified in `functionName` together with their values.

### Details

The function `priorControl` is used to specify a conjugate prior for EM within MCLUST.
Note that, as described in `defaultPrior`, in the multivariate case only 10 out of 14 models may be used in conjunction with a prior, i.e. those available in MCLUST up to version 4.4.

### Value

A list with the function name as the first component. The remaining components (if any) consist of a list of arguments to the function with assigned values.

### References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

C. Fraley and A. E. Raftery (2005). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington.

C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

### See Also

`mclustBIC`, `me`, `mstep`, `defaultPrior`

### Examples

    # default prior
    irisBIC <- mclustBIC(iris[,-5], prior = priorControl())
    summary(irisBIC, iris[,-5])

    # no prior on the mean; default prior on variance
    irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0))
    summary(irisBIC, iris[,-5])

[Package mclust version 5.3 Index]