are fundamentally different in nature from hierarchical clustering, or sparse covariance estimation. Granted, our automated procedure firstly utilizes an empirical covariance matrix generated from MCMC sampling chains. However, whereas sparse covariance estimation seeks to estimate the non- zero elements of the underlying covariance structure (Cai and Liu, 2011), our procedure concerns the non-trivial (correlated) elements, with little concern for the smaller entries. Our blocking algorithm also makes use of the complete linkage clustering algorithm, for determining groupings of correlated model parameters. Clustering algorithms have been applied to a wide variety of problems (Xu and Wunsch, 2005), but not to parameters of hierarchical models specifically with the aim of accounting for trade-offs between MCMC algorithmic efficiency and computational requirements, to produce a computationally efficient MCMC algorithm. This is a fundamentally different goal than merely producing groupings of “similar” parameters (given some measure of similarity), as is generally the goal in most clustering applications. Thereby, the existing literature on these subjects is related, but not intimately applicable to our problem at hand. A deeper consideration of these topics may be worthwhile, but we consider it beyond the scope of this paper. Reasonably straightforward improvements could be made to our automated blocking procedure, which is presented as a sensible first approach that addresses the factors affecting MCMC algorithm efficiency. By design, our procedure natively accounts for differences in system platform or architec- ture that may affect the relative efficiencies of MCMC algorithms. We can envision a wide variety of possible extensions to our algorithm, ranging from only re-blocking the slowest mixing parameter on each iteration, to permitting cuts at different heights on distinct branches of the hierarchical clustering tree. Our procedure is intended as a proof-of-concept for the automated generation of efficient MCMC algorithms, and to serve as a starting point for subsequent research. This work was supported by the NSF under grant DBI-1147230. Acknowledgements 29