Bayesian model comparison (BMC) offers a principled approach for assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then use our method to compare four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. In this application, we corroborate evidence for the recently proposed L\'evy flight model of decision-making and show how transfer learning can be leveraged to enhance training efficiency. Reproducible code for all analyses is provided.

翻译：贝叶斯模型比较（BMC）为评估竞争性计算模型的相对优劣以及将不确定性传播到模型选择决策中提供了一种原则性方法。然而，对于广受欢迎的层次模型类别，由于其高维嵌套参数结构，BMC通常难以处理。为了解决这一困难，我们提出了一种深度学习方法，适用于任何可实例化为概率程序的层次模型集合的BMC。由于我们的方法实现了摊销推断，因此在任何实际数据应用之前，能够高效地重新估计后验模型概率并快速验证性能。在一系列广泛的验证研究中，我们将该方法与最先进的桥接采样方法进行性能基准测试，并展示了在所有BMC设置下出色的摊销推断能力。随后，我们使用该方法比较了四种层次证据累积模型，这些模型由于部分隐式似然而先前被认为难以进行BMC。在此应用中，我们证实了最近提出的决策列维飞行模型的证据，并展示了如何利用迁移学习来提高训练效率。所有分析的可复现代码均已提供。