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Missing data in amortized simulation-based neural posterior estimation.
PLoS Comput. Biol. 20:e1012184 (2024)
Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates. In this work, we discuss various ways of encoding missing data and integrate them into the training and inference process. We implement the approaches in the BayesFlow methodology, an amortized estimation framework based on invertible neural networks, and evaluate their performance on multiple test problems. We find that an approach in which the data vector is augmented with binary indicators of presence or absence of values performs the most robustly. Indeed, it improved the performance also for the simpler problem of data sets with variable length. Accordingly, we demonstrate that amortized simulation-based inference approaches are applicable even with missing data, and we provide a guideline for their handling, which is relevant for a broad spectrum of applications.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Systems
ISSN (print) / ISBN
1553-734X
e-ISSN
1553-7358
Journal
PLoS Computational Biology
Quellenangaben
Volume: 20,
Issue: 6,
Article Number: e1012184
Publisher
Public Library of Science (PLoS)
Publishing Place
1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Joachim Herz Stiftung
German Research Foundation (DFG) under Germany's Excellence Strategy
German Federal Ministry of Education and Research (BMBF)
German Research Foundation (DFG) under Germany's Excellence Strategy
German Federal Ministry of Education and Research (BMBF)