Bayesian inference has become an important framework for calibrating complex ecological and environmental models. Markov-Chain Monte Carlo (MCMC) algorithms are the methodological backbone of this framework, but they are not easily parallelizable and can thus not make optimal use of modern computer architectures. A possible solution is the use of Sequential Monte Carlo (SMC) algorithms. Currently, SMCs are used mainly for Bayesian state updating, for example in weather forecasting, and are thought to be less efficient for parameter calibration than MCMCs. Unlike MCMCs, however, SMCs are easily parallelizable. Thus, SMCs may become an interesting alternative when modelers have access to parallel computing environments. The purpose of this paper is to provide an introduction to SMC algorithms for Bayesian model calibration, and to explore the trade-off between efficiency and parallelizability for MCMC and SMC algorithms. To that end, we discuss different SMC variants, and benchmark them against a state-of-the-art MCMC algorithm by calibrating three ecological models of increasing complexity. Our results show that, with appropriately chosen settings, SMCs can be faster than state-of-the-art MCMC algorithms when a sufficiently large number of parallel cores are available and when the model runtime is large compared to communication overhead for parallelization (on our hardware, a model runtime of 20 ms was enough to favor SMC algorithms). Efficient SMC settings were characterized by a balanced mix of SMC filtering and MCMC mutation steps, suggesting that mixing MCMC and SMC principles may be ideal for creating efficient and parallelizable calibration algorithms. The algorithms used in this study are provided within the BayesianTools R package for Bayesian inference with complex ecological models.

贝叶斯推理已成为校准复杂生态和环境模型的重要框架。马尔可夫链蒙特卡罗 (MCMC) 算法是该框架的方法论支柱，但它们不易并行化，因此无法最佳利用现代计算机体系结构。一种可能的解决方案是使用顺序蒙特卡罗 (SMC) 算法。目前，SMC 主要用于贝叶斯状态更新，例如在天气预报中，并且被认为在参数校准方面的效率低于 MCMC。然而，与 MCMC 不同，SMC 很容易并行化。因此，当建模者可以访问并行计算环境时，SMC 可能成为一个有趣的替代方案。本文的目的是介绍用于贝叶斯模型校准的 SMC 算法，并探索 MCMC 和 SMC 算法的效率和可并行性之间的权衡。为此，我们讨论了不同的 SMC 变体，并通过校准三个日益复杂的生态模型，将它们与最先进的 MCMC 算法进行对比。Our results show that, with appropriately chosen settings, SMCs can be faster than state-of-the-art MCMC algorithms when a sufficiently large number of parallel cores are available and when the model runtime is large compared to communication overhead for parallelization (on our硬件，20 毫秒的模型运行时间足以支持 SMC 算法）。高效的 SMC 设置的特点是 SMC 过滤和 MCMC 突变步骤的平衡混合，这表明混合 MCMC 和 SMC 原则可能是创建高效且可并行化的校准算法的理想选择。