标题： 通过比率估计的动态无似然推断（DIRE） 显示英文标题

标题： Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)

摘要： 以随机数据生成过程为基准的参数统计模型在广泛的科学领域中得到应用，因为它们能够实现精确建模。 然而，从观测数据中学习参数通常非常困难，因为其似然函数通常是难以处理的。 已经提出了无需似然的贝叶斯推断方法，包括近似贝叶斯计算（ABC）框架、合成似然及其最近的推广方法，该方法通过比率估计进行无需似然的推断（LFIRE）。 所有这些方法中的一个主要困难是选择能够降低数据维度以促进推断的总结统计量。 虽然已经为ABC提出了一些选择总结统计量的方法，但到目前为止，关于合成似然和LFIRE的文献非常稀少。 我们在此填补这一文献空白，重点关注时间序列模型的重要特殊情况。 我们表明，从数据预测输入参数的卷积神经网络为LFIRE提供了合适的总结统计量。 在一系列时间序列模型上，单一的神经网络架构产生的后验概率与替代方法相当或更准确。 显示英文摘要

摘要： Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable accurate modeling. However, learning the parameters from observed data is generally very difficult because their likelihood function is typically intractable. Likelihood-free Bayesian inference methods have been proposed which include the frameworks of approximate Bayesian computation (ABC), synthetic likelihood, and its recent generalization that performs likelihood-free inference by ratio estimation (LFIRE). A major difficulty in all these methods is choosing summary statistics that reduce the dimensionality of the data to facilitate inference. While several methods for choosing summary statistics have been proposed for ABC, the literature for synthetic likelihood and LFIRE is very thin to date. We here address this gap in the literature, focusing on the important special case of time-series models. We show that convolutional neural networks trained to predict the input parameters from the data provide suitable summary statistics for LFIRE. On a wide range of time-series models, a single neural network architecture produced equally or more accurate posteriors than alternative methods.