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which makes use of state transition matrices 

and observation matrices 

## 4 Performance Results 

<img src='content_image/296193.jpg'>

<img src='content_image/296192.jpg'>

We now present the performance of various computational strategies for MCMC estimation applied to the three example capture-recapture models. We do not present posterior results, but instead only the algorithmic efficiencies of each computational approach to generating these. For each, the posterior results of top-level parameters closely agree with existing published analyses of the same datasets and models (Lebreton et al., 1992; K´ ery and Schaub, 2012; Amstrup, McDonald, and Manly, 2010), which provides validation of our computational methodologies. 

We include results for the following computational strategies MCMC estimation: latent state MCMC (“Latent State”) where model parameters and latent states undergo MCMC sampling, filtering MCMC (“Filtering”) in which we filter over latent states and only top-level parameters undergo MCMC sampling, and a combination of filtering and blocking (“Filtering & Blocking”) in which a customized blocking strategy is used for MCMC sampling of top- level parameters. When appropriate, we also use a reduced representation (“RR”) of the 

