Center for Cosmology and Particle Physics, New York University, New York, NY 10003;
Center for Data Science, New York University, New York, NY 10003.
Proc Natl Acad Sci U S A. 2020 Mar 10;117(10):5242-5249. doi: 10.1073/pnas.1915980117. Epub 2020 Feb 20.
Simulators often provide the best description of real-world phenomena. However, the probability density that they implicitly define is often intractable, leading to challenging inverse problems for inference. Recently, a number of techniques have been introduced in which a surrogate for the intractable density is learned, including normalizing flows and density ratio estimators. We show that additional information that characterizes the latent process can often be extracted from simulators and used to augment the training data for these surrogate models. We introduce several loss functions that leverage these augmented data and demonstrate that these techniques can improve sample efficiency and quality of inference.
模拟器通常可以对真实世界现象进行很好的描述。然而，它们隐含定义的概率密度往往难以处理，导致推断出现具有挑战性的反问题。最近，已经引入了许多技术，其中包括学习不可处理密度的替代方法，包括归一化流和密度比估计器。我们表明，通常可以从模拟器中提取出描述潜在过程的附加信息，并将其用于扩充这些替代模型的训练数据。我们引入了几个利用这些扩充数据的损失函数，并证明这些技术可以提高样本效率和推断质量。
