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Wang, Z.* ; Hasenauer, J. ; Schälte, Y.

Missing data in amortized simulation-based neural posterior estimation.

PLoS Comput. Biol. 20:e1012184 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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
Corresponding Author
Keywords Systems
ISSN (print) / ISBN 1553-734X
e-ISSN 1553-7358
Journal PLoS Computational Biology
Quellenangaben Volume: 20, Issue: 6, Pages: , Article Number: e1012184 Supplement: ,
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)