Neural networks have recently shown promise for likelihood-free inference, providing orders-of-magnitude speed-ups over classical methods. However, current implementations are suboptimal when estimating parameters from independent replicates. In this paper, we use a decision-theoretic framework to argue that permutation-invariant neural networks are ideally placed for constructing Bayes estimators for arbitrary models, provided that simulation from these models is straightforward. We illustrate the potential of these estimators on both conventional spatial models, as well as highly parameterised spatial-extremes models, and show that they considerably outperform neural estimators that do not account for replication appropriately in their network design. At the same time, they are highly competitive and much faster than traditional likelihood-based estimators. We apply our estimator on a spatial analysis of sea-surface temperature in the Red Sea where, after training, we obtain parameter estimates, and uncertainty quantification of the estimates via bootstrap sampling, from hundreds of spatial fields in a fraction of a second.
翻译:神经网络最近表现出了无概率推断的希望,为古典方法提供了磁度定级加速率。 但是,在估算独立复制的参数时,目前的实施并不理想。 在本文中,我们使用一个决策理论框架来论证,在利用这些模型进行模拟后,建造贝耶斯任意模型的测深器是理想的,条件是这些模型的模拟是直截了当的。我们展示了这些测深器在常规空间模型和高度参数化的空间极距模型上的潜力,并表明它们大大超出在网络设计中不能说明适当复制的神经估计器。同时,它们具有高度竞争力,而且比传统的概率估计器要快得多。我们用我们的测算器对红海海海的海平面温度进行空间分析,在那里,经过培训后,我们获得了参数估计,并通过采样器取样对估计数进行不确定的量化,从数以百计的空间场的次数以秒计。