Many scientific models are composed of multiple discrete components, and
scien tists often make heuristic decisions about which components to include.
Bayesian inference provides a mathematical framework for systematically
selecting model components, but defining prior distributions over model
components and developing associated inference schemes has been challenging. We
approach this problem in an amortized simulation-based inference framework: We
define implicit model priors over a fixed set of candidate components and train
neural networks to infer joint probability distributions over both, model
components and associated parameters from simulations. To represent
distributions over model components, we introduce a conditional mixture of
multivariate binary distributions in the Grassmann formalism. Our approach can
be applied to any compositional stochastic simulator without requiring access
to likelihood evaluations. We first illustrate our method on a simple time
series model with redundant components and show that it can retrieve joint
posterior distribution over a set of symbolic expressions and their parameters
while accurately capturing redundancy with strongly correlated posteriors. We
then apply our approach to drift-diffusion models, a commonly used model class
in cognitive neuroscience. After validating the method on synthetic data, we
show that our approach explains experimental data as well as previous methods,
but that our fully probabilistic approach can help to discover multiple
data-consistent model configurations, as well as reveal non-identifiable model
components and parameters. Our method provides a powerful tool for data-driven
scientific inquiry which will allow scientists to systematically identify
essential model components and make uncertainty-informed modelling decisions.

本研究提出了一种基于后验模拟的隐式模型先验和神经网络的元均账后验推断方法，用于系统地选择模型组件以及实现参数推断。研究表明此方法对于发现数据一致的模型配置，估计参数以及识别非可识别模型组件等方面能够提供有力的工具，有效地促进了数据驱动模型的复杂性建造与推断。