Here is a better way of doing this, directly using the Weibull distribution function in MCMC. This is based on example 1 in the User's Guide. This gives very good mixing and uncorrelated values. The parameter estimates are very close to the theoretical. data x; run; proc mcmc data=x outpost=simoutweib seed=23 nmc=100000 statistics=(summary interval) ; ods exclude nobs; parm x; prior x ~ weibull(0,&shape,&scale); model general(0); run; proc lifereg data=simoutweib; model x=/dist=weibull; quit;
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