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Updated: Jun 23, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Missing data in amortized simulation-based neural posterior estimation.

Zijian Wang¹, Jan Hasenauer^1,2, Yannik Schälte^1,2,3

¹University of Bonn, Life and Medical Sciences Institute, Bonn, Germany.

Plos Computational Biology

|June 17, 2024

Summary

This summary is machine-generated.

Amortized simulation-based inference, a machine learning method for parameter estimation, can now handle missing data. Augmenting data with indicators of missingness proved most robust, enabling broader applications.

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Area of Science:

Machine Learning
Computational Statistics
Scientific Computing

Background:

Amortized simulation-based neural posterior estimation offers computational efficiency for parameter estimation.
Existing methods struggle with missing data, a common issue in experimental studies, potentially leading to inaccurate posterior estimates.

Purpose of the Study:

To adapt amortized simulation-based inference for handling missing data.
To evaluate different methods for encoding missing data within the BayesFlow framework.

Main Methods:

Investigated various strategies for encoding missing data within the training and inference processes.
Implemented and tested these approaches using the BayesFlow methodology, which utilizes invertible neural networks.
Evaluated performance on multiple test problems, including datasets with variable lengths.

Main Results:

Augmenting data vectors with binary indicators for value presence/absence demonstrated the most robust performance.
This approach improved accuracy and applicability for datasets with missing values.
Performance gains were also observed for datasets of variable length.

Conclusions:

Amortized simulation-based inference is applicable even with missing experimental data.
Augmenting data with missingness indicators provides a reliable guideline for handling such data.
This advancement broadens the applicability of these powerful inference techniques across diverse scientific fields.