Conjugate Sampling 
Conjugate prior is a family of prior distributions in which the prior and the posterior distributions are of the same family of distributions. For example, if you model an independently and identically distributed random variable using a normal likelihood with known variance ,
a normal prior on
is a conjugate prior because the posterior distribution of is also a normal distribution given , , , and :
Conjugate sampling is efficient because it enables the Markov chain to obtain samples from the target distribution directly. When appropriate, PROC MCMC uses conjugate sampling methods to draw conditional posterior samples. Table 54.6 lists scenarios that lead to conjugate sampling in PROC MCMC.
Family 
Parameter 
Prior 

Normal with known 
Variance 
Inverse gamma family 
Normal with known 
Precision 
Gamma family 
Normal with known scale parameter (, , or ) 
Mean 
Normal 
Multivariate normal with known 
Mean 
Multivariate normal 
Multivariate normal with known 
Covariance 
Inverse Wishart 
Multinomial 

Dirichlet 
Binomial/binary 

Beta 
Poisson 

Gamma family 
In most cases, Family in Output 54.6 refers to the likelihood function. However, it does not necessarily have to be the case. The Family is a distribution that is conditional on the parameter of interest, and it can appear in any level of the hierarchical model, including on the randomeffects level.
PROC MCMC can detect conjugacy only if the model parameter (not a function or a transformation of the model parameter) is used in the prior and Family distributions. For example, the following program leads to a conjugate sampler being used on the parameter mu:
parm mu; prior mu ~ n(0, sd=1000); model y ~ n(mu, var=s2);
However, if you modify the program slightly in the following way, although the conjugacy still holds in theory, PROC MCMC cannot detect conjugacy on mu because the parameter enters the normal likelihood function through the symbol w:
parm mu; prior mu ~ n(0, sd=1000); w = mu; model y ~ n(w, var=s2);
In this case, PROC MCMC resorts to the default sampling algorithm, which is a random walk Metropolis based on a normal kernel.
Similarly, the following statements also prevent PROC MCMC from detecting conjugacy on the parameter mu:
parm mu; prior mu ~ n(0, sd=1000); model y ~ n(mu + 2, var=s2);
When conjugacy is defected in a model, PROC MCMC performs a numerical optimization on the joint posterior distribution at the start of the MCMC simulation. To turn off this preoptimization routine, use option PROPCOV=IND.
In a normal family, an oftenused conjugate prior on the variance is
igamma(shape=0.001, scale=0.001)
An oftenused conjugate prior on the precision is
gamma(shape=0.001, iscale=0.001)
You want to exercise caution in using the igamma and gamma distributions as PROC MCMC suports both scale and iscale parametrizations in these distributions.