Automating Model Comparison in Factor Graphs
This addresses the bottleneck of manual model comparison in probabilistic programming for researchers and practitioners, though it appears incremental as it builds on existing factor graph methods.
The paper tackles the problem of automating Bayesian model comparison, which typically requires manual derivations, by developing a message passing approach on factor graphs with custom mixture nodes. This enables simultaneous parameter/state inference and model comparison, shortening the model design cycle and allowing extensions to hierarchical and temporal priors.
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.