in existing application-specific software, but are not generally available for incorpo- ration in arbitrary model structures. Using the NIMBLE package for R, we develop and test combined computational approaches using three examples from ecological capture-recapture, although our methods are generally applicable to any embedded discrete HMMs. These combinations provide several orders of magnitude improvement in MCMC sampling efficiency, defined as the rate of generating effectively indepen- dent posterior samples. In addition to being computationally significant for this class of hierarchical models, this result underscores the potential for vast improvements to MCMC sampling efficiency which can result from combinations of known algorithms. 

## Keywords: 

Capture-recapture, Effective sample size, Hidden Markov model, Hierarchical model, MCMC, NIMBLE, Sampling efficiency 

