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 showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. Additionally, we demonstrate how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method.

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在模型选择决策中传播不确定性提供了一种原则性方法。然而，由于分层模型具有高维嵌套参数结构，BMC 通常难以处理这类常见模型。为解决这一难题，我们提出了一种深度学习方法，可对任何可实例化为概率程序的分层模型集执行 BMC。由于我们的方法支持摊销推断，因此在实际数据应用前，能够高效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，我们将我们的方法与最先进的桥接采样方法进行基准对比，并证明在所有 BMC 设置下均能实现优异的摊销推断。接着，我们通过比较四个分层证据累积模型来展示我们的方法——这些模型此前因部分隐含似然而被认为无法通过 BMC 处理。此外，我们还展示了如何利用迁移学习提升训练效率。我们提供了所有分析的可复现代码及方法的开源实现。