The FMM procedure uses Bayesian analysis via a conjugate Gibbs sampler if the model belongs to a small class of mixture models for which a conjugate sampler is available. See the section Gibbs Sampler in Chapter 7: Introduction to Bayesian Analysis Procedures, for a general discussion of Gibbs sampling. Table 39.8 summarizes the models for which conjugate and Metropolis-Hastings samplers are available.

Table 39.8: Availability of Conjugate and Metropolis Samplers in the FMM Procedure

Effects (exclusive |
||
---|---|---|

of intercept) |
Distributions |
Available Samplers |

No |
Normal or T |
Conjugate or Metropolis-Hastings |

Yes |
Normal or T |
Conjugate or Metropolis-Hastings |

No |
Binomial, binary, Poisson, exponential |
Conjugate or Metropolis-Hastings |

Yes |
Binomial, binary, Poisson, exponential |
Metropolis-Hastings only |

The conjugate sampler enjoys greater efficiency than the Metropolis-Hastings sampler and has the advantage of sampling in terms of the natural parameters of the distribution.

You can always switch to the Metropolis-Hastings sampling algorithm in any model by adding the METROPOLIS option in the BAYES statement.