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The MCMC Procedure

Overview
PROC MCMC Compared with Other SAS Procedures
Getting Started
Simple Linear Regression The Behrens-Fisher Problem Random-Effects Model
Syntax
PROC MCMC Statement ARRAY Statement BEGINCNST/ENDCNST Statement BEGINNODATA/ENDNODATA Statements BY Statement MODEL Statement PARMS Statement PREDDIST Statement PRIOR/HYPERPRIOR Statement Programming Statements RANDOM Statement UDS Statement
Details
How PROC MCMC Works Blocking of Parameters Sampling Methods Tuning the Proposal Distribution Direct Sampling Conjugate Sampling Initial Values of the Markov Chains Assignments of Parameters Standard Distributions Usage of Multivariate Distributions Specifying a New Distribution Using Density Functions in the Programming Statements Truncation and Censoring Some Useful SAS Functions Matrix Functions in PROC MCMC Create Design Matrix Modeling Joint Likelihood Access Lag and Lead Variables CALL ODE and CALL QUAD Subroutines Regenerating Diagnostics Plots Caterpillar Plot Autocall Macros for Postprocessing Gamma and Inverse-Gamma Distributions Posterior Predictive Distribution Handling of Missing Data Functions of Random-Effects Parameters Floating Point Errors and Overflows Handling Error Messages Computational Resources Displayed Output ODS Table Names ODS Graphics
Examples
Simulating Samples From a Known Density Box-Cox Transformation Logistic Regression Model with a Diffuse Prior Logistic Regression Model with Jeffreys’ Prior Poisson Regression Nonlinear Poisson Regression Models Logistic Regression Random-Effects Model Nonlinear Poisson Regression Multilevel Random-Effects Model Multivariate Normal Random-Effects Model Missing at Random Analysis Nonignorably Missing Data (MNAR) Analysis Change Point Models Exponential and Weibull Survival Analysis Time Independent Cox Model Time Dependent Cox Model 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 One-Compartment Model with Pharmacokinetic Data
References

Details: MCMC Procedure

Subsections:

How PROC MCMC Works
Blocking of Parameters
Sampling Methods
Tuning the Proposal Distribution
Direct Sampling
Conjugate Sampling
Initial Values of the Markov Chains
Assignments of Parameters
Standard Distributions
Usage of Multivariate Distributions
Specifying a New Distribution
Using Density Functions in the Programming Statements
Truncation and Censoring
Some Useful SAS Functions
Matrix Functions in PROC MCMC
Create Design Matrix
Modeling Joint Likelihood
Access Lag and Lead Variables
CALL ODE and CALL QUAD Subroutines
Regenerating Diagnostics Plots
Caterpillar Plot
Autocall Macros for Postprocessing
Gamma and Inverse-Gamma Distributions
Posterior Predictive Distribution
Handling of Missing Data
Functions of Random-Effects Parameters
Floating Point Errors and Overflows
Handling Error Messages
Computational Resources
Displayed Output
ODS Table Names
ODS Graphics

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