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.

翻译：序贯重要性采样与序贯蒙特卡洛等序贯算法在无法直接获得似然函数的贝叶斯模型推断中已被证明具有基础性意义。对于近似贝叶斯计算而言，SMC-ABC是目前最先进的采样器。然而，由于ABC范式本身存在计算冗余，序贯ABC方案可通过精准定位的提案采样器有效规避低概率参数区域，从而获得性能提升。本研究通过开发以数据摘要统计量为条件的新型提案采样器，为ABC建模工具箱提供了新工具。从某种意义上说，所提出的参数被"引导"快速抵达与观测数据相容的后验曲面区域。这加速了序贯采样器的收敛过程，在保持推断精度的同时显著降低了计算成本。我们为SIS-ABC和SMC-ABC提供了多种基于高斯分布与连接函数的引导式采样器，可有效应对具有挑战性的案例研究，包括多峰后验分布、高度相关后验分布、约含20个参数的层次模型，以及使用超过400个摘要统计量的细胞运动模拟研究。