Search

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency using the open‐source software NIMBLE (R package nimble) using common ecological models of species occurrence and abundance as examples. We ask how MCMC efficiency depends on model formulation, model size, data, and sampling strategy. For multiseason and/or multispecies occupancy models and for N‐mixture models, we compare the efficiency of sampling discrete latent states vs. integrating over them, including more vs. fewer hierarchical model components, and univariate vs. block‐sampling methods. We include the common MCMC tool JAGS in comparisons. For simple models, there is little practical difference between computational approaches. As model complexity increases, there are strong interactions between model formulation and sampling strategy on MCMC efficiency. There is no one‐size‐fits‐all best strategy, but rather problem‐specific best strategies related to model structure and type. In all but the simplest cases, NIMBLE's default or customized performance achieves much higher efficiency than JAGS. In the two most complex examples, NIMBLE was 10–12 times more efficient than JAGS. We find NIMBLE is a valuable tool for many ecologists utilizing Bayesian inference, particularly for complex models where JAGS is prohibitively slow. Our results highlight the need for more guidelines and customizable approaches to fit hierarchical models to ensure practitioners can make the most of occupancy and other hierarchical models. By implementing model‐generic MCMC procedures in open‐source software, including the NIMBLE extensions for integrating over latent states (implemented in the R package nimbleEcology), we have made progress toward this aim. [ABSTRACT FROM AUTHOR]

Agility without objective governance cannot scale, and governance without agility cannot compete. Agile methods are mainstream, and software enterprises are adopting these practices in diverse delivery contexts and at enterprise scale. IBM's broad industry experience with agile transformations and deep internal know-how point to two key principles to deliver sustained improvements in software business outcomes with higher confidence: measure and streamline change costs, and steer with economic governance and Bayesian analytics. Applying these two principles in context is the crux of measured improvement in continuous delivery of smarter software-intensive systems. This article describes more meaningful measurement and prediction foundations for economic governance. The Web extra at http://youtu.be/ghAM8ifyeVI is a video in which Walker Royce, author, IEEE Software editorial board member, and IBM Chief Software Economist, describes how to reason about software delivery governance with lean principles. [ABSTRACT FROM PUBLISHER]