OneFlowSBI: One Model, Many Queries for Simulation-Based Inference

• URL: http://arxiv.org/abs/2601.22951v1
• Date: Fri, 30 Jan 2026 13:14:44 GMT
• Title: OneFlowSBI: One Model, Many Queries for Simulation-Based Inference
• Authors: Mayank Nautiyal, Li Ju, Melker Ernfors, Klara Hagland, Ville Holma, Maximilian Werkö Söderholm, Andreas Hellander, Prashant Singh,
• Abstract summary: textitOneFlow SBI is a unified framework for simulation-based inference.<n>It learns a single flow-matching generative model over the joint distribution of parameters and observations.<n>It supports multiple inference tasks, including posterior sampling, likelihood estimation, and arbitrary conditional distributions.
• Score: 2.614875980890442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce \textit{OneFlowSBI}, a unified framework for simulation-based inference that learns a single flow-matching generative model over the joint distribution of parameters and observations. Leveraging a query-aware masking distribution during training, the same model supports multiple inference tasks, including posterior sampling, likelihood estimation, and arbitrary conditional distributions, without task-specific retraining. We evaluate \textit{OneFlowSBI} on ten benchmark inference problems and two high-dimensional real-world inverse problems across multiple simulation budgets. \textit{OneFlowSBI} is shown to deliver competitive performance against state-of-the-art generalized inference solvers and specialized posterior estimators, while enabling efficient sampling with few ODE integration steps and remaining robust under noisy and partially observed data.

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