Ponisio Lauren C, de Valpine Perry, Michaud Nicholas, Turek Daniel
Department of Entomology University of California Riverside CA USA.
Department of Environmental Science, Policy, and Management University of California Berkeley CA USA.
Ecol Evol. 2020 Feb 14;10(5):2385-2416. doi: 10.1002/ece3.6053. eCollection 2020 Mar.
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.
马尔可夫链蒙特卡罗（Markov chain Monte Carlo, MCMC）效率的提高推动了贝叶斯分层模型在统计分析各方面的应用。随着模型复杂性及其拟合运行时间的增加，确定提高MCMC性能的策略变得愈发关键。我们以物种出现和丰度的常见生态模型为例，使用开源软件NIMBLE（R包nimble）评估提高MCMC效率的不同策略。我们探讨了MCMC效率如何依赖于模型构建、模型规模、数据和抽样策略。对于多季节和/或多物种占用模型以及N - 混合模型，我们比较了对离散潜在状态进行抽样与对其进行积分的效率，包括层次模型组件数量的多少，以及单变量抽样与块抽样方法。我们在比较中纳入了常用的MCMC工具JAGS。对于简单模型，计算方法之间几乎没有实际差异。随着模型复杂性的增加，模型构建和抽样策略对MCMC效率存在强烈的相互作用。不存在适用于所有情况的最佳策略，而是存在与模型结构和类型相关的特定问题的最佳策略。除了最简单的情况外，NIMBLE的默认或定制性能比JAGS的效率高得多。在两个最复杂的例子中，NIMBLE的效率比JAGS高10至12倍。我们发现NIMBLE对于许多使用贝叶斯推断的生态学家来说是一个有价值的工具，特别是对于JAGS运行速度过慢的复杂模型。我们的结果强调了需要更多的指导方针和可定制方法来拟合分层模型，以确保从业者能够充分利用占用模型和其他分层模型。通过在开源软件中实现通用模型的MCMC程序，包括用于对潜在状态进行积分的NIMBLE扩展（在R包nimbleEcology中实现），我们朝着这个目标取得了进展。
