approach requires the additional overhead of artificial model variables and observations. For the multistate Orchid model, a combination of filtering over latent states and dynamic block sampling of parameters yielded a 3-fold improvement in sampling efficiency of the slowest mixing parameter, relative to the latent state MCMC (Figure 2). The latent state MCMC samples 2,157 latent variables in addition to 19 top-level param- eters, which required 42 minutes to generate 100,000 samples. Efficiency results for the latent state MCMC are quite similar to the filtering MCMC, which required 36 minutes but with slightly inferior mixing. Both of these algorithms struggle to achieve good mixing among the nine state transition probabilities. We might expect triplets of these parameters to be highly correlated due to the Dirichlet prior imposing a sum-to-one constraint, and indeed, examining the posterior correlations we find several instances of absolute pairwise posterior correlation greater than 0.9. Under the latent state and filtering MCMC algorithms, several state transition probabilities have sampling efficiencies between 0.1 and 0.3 ESPS, which dictates the minimum efficiencies shown in Figure 2. 18 Figure 2: Minimum and mean parameter sampling efficiencies for the Orchid model. 4.2 Orchid Model 0.0 0.2 0.4 0.6 Sampling efficiency (ESPS) Minimum 0 2 4 6 Sampling efficiency (ESPS) Mean MCMC Algorithm Latent State (JAGS) Filtering Filtering & Blocking