The MCMC Procedure
-
Overview
-
Getting Started
-
Syntax
-
DetailsHow PROC MCMC WorksBlocking of ParametersSampling MethodsTuning the Proposal DistributionDirect SamplingConjugate SamplingInitial Values of the Markov ChainsAssignments of ParametersStandard DistributionsUsage of Multivariate DistributionsSpecifying a New DistributionUsing Density Functions in the Programming StatementsTruncation and CensoringSome Useful SAS FunctionsMatrix Functions in PROC MCMCCreate Design MatrixModeling Joint LikelihoodRegenerating Diagnostics PlotsCaterpillar PlotAutocall Macros for PostprocessingGamma and Inverse-Gamma DistributionsPosterior Predictive DistributionHandling of Missing DataFunctions of Random-Effects ParametersFloating Point Errors and OverflowsHandling Error MessagesComputational ResourcesDisplayed OutputODS Table NamesODS Graphics
-
ExamplesSimulating Samples From a Known DensityBox-Cox TransformationLogistic Regression Model with a Diffuse PriorLogistic Regression Model with Jeffreys’ PriorPoisson RegressionNonlinear Poisson Regression ModelsLogistic Regression Random-Effects ModelNonlinear Poisson Regression Multilevel Random-Effects ModelMultivariate Normal Random-Effects ModelMissing at Random AnalysisNonignorably Missing Data (MNAR) AnalysisChange Point ModelsExponential and Weibull Survival AnalysisTime Independent Cox ModelTime Dependent Cox ModelPiecewise Exponential Frailty ModelNormal Regression with Interval CensoringConstrained AnalysisImplement a New Sampling AlgorithmUsing a Transformation to Improve MixingGelman-Rubin Diagnostics
- References
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
- 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