Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked 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：贝叶斯状态与参数估计已在多种概率编程语言中得到有效自动化。然而，模型比较过程虽至关重要却常被忽视，因其仍依赖易出错且耗时的手动推导。本文通过采用自定义混合节点的Forney风格因子图消息传递机制，高效实现了贝叶斯模型平均、选择与组合的自动化。借助含比例因子的消息传递，参数与状态推断及模型比较可同步执行。该方法缩短了模型设计周期，并可便捷地扩展至分层与时序模型先验，从而适应复杂时变过程的建模需求。