论文标题
顽固性贝叶斯模型的指导顺序ABC方案
Guided sequential ABC schemes for intractable Bayesian models
论文作者
论文摘要
顺序算法（例如顺序重要性采样（SIS）和顺序蒙特卡洛（SMC））在贝叶斯推断中已证明基本的模型，不承认易于获得的可能性函数。对于近似贝叶斯计算（ABC），SMC-ABC是最新的采样器。但是，由于ABC范式本质上是浪费的，因此顺序的ABC方案可以从靶向良好的提案采样器中受益，这些提案采样器有效地避免了不可能的参数区域。我们用新颖的提案采样器为ABC Modeller的工具箱做出了贡献，这是数据的摘要统计数据。从某种意义上说，所提出的参数被“指导”到与观察到的数据兼容的后表面的迅速到达区域。这加快了这些顺序采样器的收敛性，从而减少了计算工作，同时保留了推断的准确性。我们为SIS-ABC和SMC-ABC提供了各种指导性高斯和基于Copula的采样器，并为具有挑战性的病例研究提供了缓解推理，包括多模式后期，高度相关的后代，具有约20个参数的层次模型，以及使用更多400个摘要统计的细胞移动的模拟研究。
Sequential algorithms such as sequential importance sampling (SIS) and sequential Monte Carlo (SMC) have proven fundamental in Bayesian inference for models not admitting a readily available likelihood function. For approximate Bayesian computation (ABC), SMC-ABC is the state-of-art sampler. However, since the ABC paradigm is intrinsically wasteful, sequential ABC schemes can benefit from well-targeted proposal samplers that efficiently avoid improbable parameter regions. We contribute to the ABC modeller's toolbox with novel proposal samplers that are conditional to summary statistics of the data. In a sense, the proposed parameters are "guided" to rapidly reach regions of the posterior surface that are compatible with the observed data. This speeds up the convergence of these sequential samplers, thus reducing the computational effort, while preserving the accuracy in the inference. We provide a variety of guided Gaussian and copula-based samplers for both SIS-ABC and SMC-ABC easing inference for challenging case-studies, including multimodal posteriors, highly correlated posteriors, hierarchical models with about 20 parameters, and a simulation study of cell movements using more than 400 summary statistics.
