situation arises where the final iteration produces a different MCMC algorithm with efficiency infe- rior to that of the previous iteration, then prudence would suggest a thoughtful examination of the posterior samples, empirical correlation matrices, properties of the adapted samplers, convergence diagnostics, etc. We now compare the performance of MCMC algorithms produced using the automated blocking procedure of section 3 against various static MCMC algorithms. First, we describe the computing environment in which our analyses are performed. We then describe a broadly representative suite of example models, and present the performance results of automated and static MCMC algorithms for each. A public Github repository containing scripts for reproducing our results may be found at https://github.com/danielturek/automated-blocking-examples . Since one of our points is that optimal design of MCMC algorithms depends on the computing environment, we briefly summarize the software tools and computing platform used. All statistical models and MCMC algorithms were built using the NIMBLE package (NIMBLE Development Team, 2014) for R (R Core Team, 2014). NIMBLE allows hierarchical models to be defined within R using the BUGS model declaration syntax introduced by the BUGS project (Lunn et al., 2000; Lunn et al., 2012). MCMC algorithms in NIMBLE are written using NIMBLE’s domain specific language for specifying hierarchical model algorithms. This language is an enhanced subset of R (interfaced through an R session) which is compiled into C++ code, which is subsequently compiled and run. As a result, the examples here use highly efficient code generated automatically for each model and algorithm. Of particular importance is that matrix operations are done via the highly optimized C++ Eigen library (Guennebaud and Jacob, 2010). Finally, the high-level programmability provided by R facilitated the dynamic exploration of MCMC algorithms. Examples were run using R version 3.1.2, using the BLAS (Basic Linear Algebra Subprograms) provided by R for multivariate density 4 Automated Blocking Performance 4.1 Computing Environment 17