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• SAS/STAT Procedures A-Z

The MCMC Procedure

• a general purpose Markov chain Monte Carlo (MCMC) simulation procedure that is designed to fit Bayesian models
• allows you to specify a likelihood function for the data, prior distributions for the parameters, and hyperprior distributions if you are fitting hierarchical models
• 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
• analyze data that have any likelihood, prior, or hyperprior as long as these functions are programmable using the SAS data step functions
• parameters can enter the model linearly or in any nonlinear functional form
• uses an adaptive blocked random-walk Metropolis algorithm that uses a normal proposal distribution by default
• permits the use of a user defined sampler as an alternative to the default random-walk Metropolis algorithm
• obtain separate analyses on observations in groups
• uses ODS to create a SAS data set corresponding to any table
• supports ODS Graphics

For further details see the SAS/STAT User's Guide: The MCMC Procedure
( PDF | HTML )

Examples

• Simulating Samples From a Known Density
• Box-Cox Transformation
• Logistic Regression Model with a Diffuse Prior
• Logistic Regression Model with Jeffrey's Prior
• Poisson Regression
• Nonlinear Poisson Regression Models
• Logistic Regression Random-Effects Models
• Nonlinear Poisson Regression Random-Effects Models
• Multivariate Normal Random-Effects Model
• Change Point Models
• Exponential and Weibull Survival Analysis
• Time Independent Cox Models
• Time Dependent Cox Models
• Piecewise Exponential Frailty Model
• Normal Regression with Interval Censoring
• Constrained Analysis
• Implement a New Sampling Algorithm
• Using a Transformation to Improve Mixing
• Gelman-Rubin Diagnostics

• Bayesian Zero-Inflated Poisson Regression
  PDF (616KB)  |   HTML

• Bayesian Exponential Mixture Model
  PDF (444KB)  |   HTML

• Bayesian Linear Regression with Standardized Covariates
  PDF (307KB)  |   HTML

• Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data
  Using SAS/STAT 9.2
  PDF (348KB)  |   HTML
  

• Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data
  Using SAS/STAT 9.3
  PDF (348KB)  |   HTML
  

• Bayesian Binomial Model with Power Prior Using the MCMC Procedure
  PDF (348KB)  |   HTML
  

• Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.2
  PDF (348KB)  |   HTML
  

• Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.3
  PDF (348KB)  |   HTML
  

• Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.2
  PDF (348KB)  |   HTML
  

• Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.3
  PDF (348KB)  |   HTML
  

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