The MCMC Procedure |

Initial Values of the Markov Chains |

You can assign initial values to any parameters. To assign initial values, you can either use the PARMS statements or use programming statements within the BEGINCNST and ENDCNST statements. For the latter approach, see the section BEGINCNST/ENDCNST Statement.

When parameters have missing initial values, PROC MCMC tries to generate them from the respective prior distributions, as long as the distributions are listed in the section Standard Distributions. PROC MCMC either uses the mode from the prior distribution or draws a random number from it. For distributions that do not have modes, such as the uniform distribution, PROC MCMC uses the mean instead. In general, PROC MCMC avoids using starting values that are close to the boundary of support of the prior distribution. For example, the exponential prior has a mode at , and PROC MCMC starts an initial value at the mean. This avoids some potential numerical problems. If you use the GENERAL or DGENERAL functions in the PRIOR statements, you must provide initial values for those parameters.

If you use the optimization option PROPCOV, PROC MCMC starts the tuning at the optimized values. The procedure overwrites the initial values that you provided unless you use the option INIT=REINIT.

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