论文标题
对昂贵的时间序列模拟器的摊销无可能推断，并具有标志性比率估算
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
论文作者
论文摘要
自然和社会科学中复杂动态的仿真模型通常缺乏可拖动的似然功能，从而使基于传统的可能性基于传统的统计推断不可能。机器学习的最新进展引入了新型算法，用于使用基于二进制分类器的可能性比率的技巧来估算原本棘手的似然函数。因此，只要构建良好的概率分类器，就可以获得有效的可能性近似值。我们使用基于最近引入的签名内核的路径签名为顺序数据提出了一个内核分类器。我们证明，签名的代表性能力会产生高性能的分类器，即使在至关重要的情况下，样本数量较低的情况也是如此。在这种情况下，对于常见的后推理任务，我们的方法可以优于复杂的神经网络。
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel algorithms for estimating otherwise intractable likelihood functions using a likelihood ratio trick based on binary classifiers. Consequently, efficient likelihood approximations can be obtained whenever good probabilistic classifiers can be constructed. We propose a kernel classifier for sequential data using path signatures based on the recently introduced signature kernel. We demonstrate that the representative power of signatures yields a highly performant classifier, even in the crucially important case where sample numbers are low. In such scenarios, our approach can outperform sophisticated neural networks for common posterior inference tasks.
