标题： 适应ABC距离函数 显示英文标题

标题： Adapting the ABC distance function

摘要： 近似贝叶斯计算（ABC）为似然函数难以计算或无法计算的模型提供近似推断。它通过针对各种参数值模拟模型，并接受与观测值足够接近的结果来实现。关于应使用哪些数据摘要统计量来判断接近程度已有许多进展，但对于如何为其赋权的研究相对较少。通常在算法开始时选择权重以使摘要统计量在相似尺度上变化。然而，在参数建议分布更新的迭代ABC算法中，这些初始权重可能不合适。这会显著改变摘要统计量的分布，因此需要不同的归一化权重。本文提出了两种自适应更新权重的迭代ABC算法，并在测试应用中展示了改进的结果。 显示英文摘要

摘要： Approximate Bayesian computation performs approximate inference for models where likelihood computations are expensive or impossible. Instead simulations from the model are performed for various parameter values and accepted if they are close enough to the observations. There has been much progress on deciding which summary statistics of the data should be used to judge closeness, but less work on how to weight them. Typically weights are chosen at the start of the algorithm which normalise the summary statistics to vary on similar scales. However these may not be appropriate in iterative ABC algorithms, where the distribution from which the parameters are proposed is updated. This can substantially alter the resulting distribution of summary statistics, so that different weights are needed for normalisation. This paper presents two iterative ABC algorithms which adaptively update their weights and demonstrates improved results on test applications.