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| The MCMC Procedure |
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- Overview: MCMC Procedure
- Getting Started: MCMC Procedure
- Syntax: MCMC Procedure
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Details: MCMC Procedure
- How PROC MCMC Works
- Blocking of Parameters
- Samplers
- Tuning the Proposal Distribution
- Initial Values of the Markov Chains
- Assignments of Parameters
- Standard Distributions
- Specifying a New Distribution
- Using Density Functions in the Programming Statements
- Truncation and Censoring
- Multivariate Density Functions
- Some Useful SAS Functions
- Matrix Functions in PROC MCMC
- Modeling Joint Likelihood
- Regenerating Diagnostics Plots
- Posterior Predictive Distribution
- Handling of Missing Data
- Floating Point Errors and Overflows
- Handling Error Messages
- Computational Resources
- Displayed Output
- ODS Table Names
- ODS Graphics
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Examples: MCMC Procedure
- Simulating Samples From a Known Density
- Box-Cox Transformation
- Generalized Linear Models
- Nonlinear Poisson Regression Models
- Random-Effects Models
- Change Point Models
- Exponential and Weibull Survival Analysis
- Cox Models
- Normal Regression with Interval Censoring
- Constrained Analysis
- Implement a New Sampling Algorithm
- Using a Transformation to Improve Mixing
- Gelman-Rubin Diagnostics
- References
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Copyright © 2009 by SAS Institute Inc., Cary, NC, USA. All rights reserved.