The HPFMM Procedure

Conjugate Sampling

The HPFMM 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 SAS/STAT 13.2 User's Guide in Chapter 7: Introduction to Bayesian Analysis Procedures in SAS/STAT 13.2 User's Guide, for a general discussion of Gibbs sampling. Table 6.8 summarizes the models for which conjugate and Metropolis-Hastings samplers are available.

Table 6.8: Availability of Conjugate and Metropolis Samplers in the HPFMM 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.