Automating Model Comparison in Factor Graphs

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 pap...

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Bibliographic Details
Published in:arXiv.org 2023-07
Main Authors: Bart van Erp, Nuijten, Wouter W L, Thijs van de Laar, de Vries, Bert
Format: Article
Language:English
Subjects:
Bayesian analysis
Mathematical models
Message passing
Parameter estimation
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2306.05965