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我现在正在sas实习，关于您这个问题，现在在sas9.2中已经可以解决，在SAS/STAT模块中有新开发有一个procedure是MCMC，这是关于MCMC的官方说明
The MCMC procedure is a general purpose Markov chain Monte Carlo (MCMC) simulation procedure that is designed to fit Bayesian models. Bayesian statistics is different from traditional statistical methods such as frequentist or classical methods. For a short introduction to Bayesian analysis and related basic concepts, see Chapter 7, Introduction to Bayesian Analysis Procedures. Also see the section A Bayesian Reading List for a guide to Bayesian textbooks of varying degrees of difficulty.
In essence, Bayesian statistics treats parameters as unknown random variables, and it makes inferences based on the posterior distributions of the parameters. There are several advantages associated with this approach to statistical inference. Some of the advantages include its ability to use prior information and to directly answer specific scientific questions that can be easily understood. For further discussions of the relative advantages and disadvantages of Bayesian analysis, see the section Bayesian Analysis: Advantages and Disadvantages.
It follows from Bayes’ theorem that a posterior distribution is the product of the likelihood function and the prior distribution of the parameter. In all but the simplest cases, it is very difficult to obtain the posterior distribution directly and analytically. Often, Bayesian methods rely on simulations to generate sample from the desired posterior distribution and use the simulated draws to approximate the distribution and to make all of the inferences.
PROC MCMC is a flexible simulation-based procedure that is suitable for fitting a wide range of Bayesian models. To use the procedure, you need to specify a likelihood function for the data and a prior distribution for the parameters. You might also need to specify hyperprior distributions if you are fitting hierarchical models. PROC MCMC then obtains samples from the corresponding posterior distributions, produces summary and diagnostic statistics, and saves the posterior samples in an output data set that can be used for further analysis. You can analyze data that have any likelihood, prior, or hyperprior with PROC MCMC, as long as these functions are programmable using the SAS DATA step functions. The parameters can enter the model linearly or in any nonlinear functional form. The default algorithm that PROC MCMC uses is an adaptive blocked random walk Metropolis algorithm that uses a normal proposal distribution.
具体的操作可以见http://support.sas.com/documentation/cdl/en/statug/59654/HTML/default/mcmc_toc.htm
这个MCMC程序还在实验过程中 具体操作 可以见SAS GLOBE Forum09的论文（附件就是）
不过SAS9.2还没有破解版，虽然偶现在用的是sas9.2 不过还得遵守公司保密协议
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