Bayesian state and parameter estimation have been automated effectively in the literature, however, this has not yet been the case for model comparison, which therefore still requires error-prone and time-consuming manual derivations. As a result, model comparison is often overlooked and ignored, despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.

翻译：贝叶斯状态和参数估计已在文献中得到有效自动化，但模型比较尚未实现这一点，因此仍需进行易出错且耗时的手工推导。结果，尽管模型比较至关重要，却常被忽视和忽略。本文通过在一个带有自定义混合节点的福尼式因子图上进行消息传递，高效地自动化了贝叶斯模型平均、选择和组合。参数和状态推断以及模型比较随后可借助比例因子通过消息传递同时执行。该方法缩短了模型设计周期，并允许直接扩展到分层和时变模型先验，以应对复杂时变过程的建模需求。