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 Chapter 7: Introduction to Bayesian Analysis Procedures, for a general discussion of Gibbs sampling. Table 51.8 summarizes the models for which conjugate and Metropolis-Hastings samplers are available.
Table 51.8: 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.