(1997) and follow the analysis of Zipunnikov and Booth (2006). The dataset contains 968 counts of senior-citizen clinical visits, which are modeled as Poisson counts. The linear predictor contains fixed and random effects, using a variety of covariates and including several interaction terms. We present three quantities to gauge the performance of MCMC algorithm Ψ. Rather than algo- rithmic efficiency A (Ψ), for convenience of interpretation we present the proportional quantity ESS = 10,000 A (Ψ), where ESS denotes effective sample size. This scaling of A (Ψ) has a natural inter- pretation as the number of effective samples (for the slowest mixing parameter) which result from a chain of 10,000 MCMC samples. Similarly, to represent the computational requirement C (Ψ), we present the proportional quantity Runtime = 10,000 C (Ψ), interpretable as the time (in seconds) required to generate 10,000 MCMC samples. We directly present the overall MCMC efficiency as Efficiency = ESS / Runtime = A (Ψ) /C (Ψ) = E (Ψ), which is independent of any scaling, and maintains the intuitive interpretation as the number of effective samples generated per second of algorithm runtime (again, for the slowest mixing parameter). MCMC sampling is performed using a fixed random number seed and identical initial values for each model, so identical MCMC algorithms will produce identical sample chains, and hence ESS, but not necessarily Runtime or Efficiency on account of discrepancies in algorithm runtime. We observe the automated procedure producing the same MCMC algorithm across repeated experiments, with numerical results for Runtime and Efficiency varying less than 5% from those presented herein. For each example model M , we present results for MCMC algorithm Ψ$_{block}$ denoted as “All Blocked,” and those of Ψ$_{scalar}$ as “All Scalar,” noting that Ψ$_{scalar}$ also represents the initial state (0 th iteration) of the automated blocking procedure. The maximally efficient algorithm generated via automated blocking is presented as “Auto Blocking,” which will generally represent a dynam- ically determined blocking scheme. We also present a third static MCMC algorithm, which is not necessarily a member of Ψ$_{M}$ on account of the possible use of conjugate sampling. This algorithm assigns block samplers to groups of parameters arising from multivariate distributions, scalar sam- plers to parameters arising from univariate distributions, and assigns conjugate samplers whenever 21 4.3 Performance Results