论文标题
黑盒发电机上的对抗性可能性的推断
Adversarial Likelihood-Free Inference on Black-Box Generator
论文作者
论文摘要
生成对抗网络（GAN）可以看作是数据分布的隐式估计器，并且该视角在黑框生成器的真实输入参数估计中使用对抗概念激发了使用对手概念。尽管以前的无可能推理作品在发电机输入上引入了隐性建议分布，但本文分析了提案分布方法的理论限制。最重要的是，我们引入了一种新的算法，对逆向可能性的推理（ALFI），以减轻分析的局限性，因此ALFI能够在黑盒生成模型的输入参数上找到后验分布。我们通过不同的仿真模型和预训练的统计模型实验了ALFI，并确定ALFI在有限的模拟预算中实现了最佳参数估计精度。
Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input parameter estimation of black-box generators. While previous works on likelihood-free inference introduces an implicit proposal distribution on the generator input, this paper analyzes theoretic limitations of the proposal distribution approach. On top of that, we introduce a new algorithm, Adversarial Likelihood-Free Inference (ALFI), to mitigate the analyzed limitations, so ALFI is able to find the posterior distribution on the input parameter for black-box generative models. We experimented ALFI with diverse simulation models as well as pre-trained statistical models, and we identified that ALFI achieves the best parameter estimation accuracy with a limited simulation budget.
