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首页> 外文会议>International Conference on Computational Methods in Systems Biology >A Scheme for Adaptive Selection of Population Sizes in Approximate Bayesian Computation - Sequential Monte Carlo
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A Scheme for Adaptive Selection of Population Sizes in Approximate Bayesian Computation - Sequential Monte Carlo

机译：近似贝叶斯计算中的人口规模自适应选择方案-顺序蒙特卡洛

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摘要

Parameter inference and model selection in systems biology often requires likelihood-free methods, such as Approximate Bayesian Computation (ABC). In recent years, this approach has frequently been combined with a Sequential Monte Carlo (ABC-SMC) scheme. In this scheme, the approximation of the posterior distribution through a population of particles is iteratively improved by a sequential sampling strategy. However, it has been difficult to give general guidelines on how to choose the size of these populations. In this manuscript, we propose a method to adaptively and automatically select these population sizes. The method exploits the cross-validated approximation error of a kernel density estimate of the particles in the current population to select the number of particles for the subsequent population. We found the proposed method to be robust to the initially chosen population size and to the number of posterior modes. We demonstrated that the method is applicable to parameter inference as well as to model selection. The study of a computationally demanding multiscale model confirmed the method's scalability. In conclusion, the proposed method is applicable to a wide range of parameter and model selection tasks. The method makes the influence of the population size on the approximation error explicit simplifying the application of ABC-SMC schemes.
机译：系统生物学中的参数推断和模型选择通常需要无可能性方法，例如近似贝叶斯计算（ABC）。近年来，这种方法经常与顺序蒙特卡洛（ABC-SMC）计划相结合。在该方案中，通过顺序采样策略迭代地改善了通过粒子总体的后验分布的近似性。但是，很难就如何选择这些人群的大小提供一般指导。在本手稿中，我们提出了一种自适应地自动选择这些人口规模的方法。该方法利用当前总体中粒子的核密度估计的交叉验证近似误差来选择后续群体的粒子数。我们发现所提出的方法对于最初选择的人口规模和后验模式的数量是鲁棒的。我们证明了该方法适用于参数推断以及模型选择。对计算要求高的多尺度模型的研究证实了该方法的可扩展性。总之，该方法适用于多种参数和模型选择任务。该方法使人口规模对近似误差的影响变得明显，从而简化了ABC-SMC方案的应用。

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