The FMM Procedure (Experimental)

Conjugate Sampling

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 for a general discussion of Gibbs sampling. Table 37.8 summarizes the models for which conjugate and Metropolis-Hastings samplers are available.

Table 37.8 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.