Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data. Recent work in cognitive science considered BOED for computational models of human behavior with tractable and known likelihood functions. However, tractability often comes at the cost of realism; simulator models that can capture the richness of human behavior are often intractable. In this work, we combine recent advances in BOED and approximate inference for intractable models, using machine-learning methods to find optimal experimental designs, approximate sufficient summary statistics and amortized posterior distributions. Our simulation experiments on multi-armed bandit tasks show that our method results in improved model discrimination and parameter estimation, as compared to experimental designs commonly used in the literature.
翻译:Bayesian最佳实验设计(BOED)是一种方法,用来确定可望产生信息数据的各种实验。在认知科学方面,最近的工作认为BOED用于计算具有可移植和已知可能性功能的人类行为的计算模型。然而,可移植性往往以现实主义为代价;能够捕捉人类行为丰富内容的模拟模型往往是棘手的。在这项工作中,我们结合了BOED的最新进展和棘手模型的近似推论,利用机器学习方法寻找最佳的实验设计、接近足够的摘要统计和摊销后传分布。我们在多臂土匪任务上的模拟实验表明,我们的方法结果与文献中常用的实验设计相比,是改进模型区分和参数估计的方法。