论文标题
在对比度学习的无可能推理
On Contrastive Learning for Likelihood-free Inference
论文作者
论文摘要
非似然方法在随机模拟器模型中执行参数推断，其中评估似然性是棘手的，但采样合成数据是可能的。此可能性问题的一类方法使用分类器来区分使用模拟器生成的参数观察样品对，并从某些参考分布中采样对，该分布隐含地学习与可能性成正比的密度比。另一种流行的方法将条件分布拟合到参数后部，并且最近的特定变体允许在此任务中使用柔性神经密度估计器。在这项工作中，我们表明，这两种方法都可以在一般的对比学习方案下统一，并澄清应如何运行和比较它们。
Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sampled from some reference distribution, which implicitly learns a density ratio proportional to the likelihood. Another popular class of methods fits a conditional distribution to the parameter posterior directly, and a particular recent variant allows for the use of flexible neural density estimators for this task. In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be run and compared.
