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Automating Model Comparison in Factor Graphs

arXiv 2306.05965 ↗First posted 9 Jun 2023cs.LG cs.AI stat.MLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 57 references that are in the map. First posted on arXiv 9 Jun 2023.

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References · 14

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  12. 2022Meta-Uncertainty in Bayesian Model Comparisoncited by 5
2 more
  1. 2014Testing hypotheses via a mixture estimation modelcited by 2
  2. 2017Bayesian model averaging via mixture model estimationcited by 2

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