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arXiv Statistics ML By Mayank Nautiyal, Li Ju, Melker Ernfors, Klara Hagland, Ville Holma, Maximilian Werk\"o S\"oderholm, Andreas Hellander, Prashant Singh

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

Read the original on arXiv Statistics ML →

OneFlowSBI is a masked flow matching framework that trains a single network on the joint distribution of parameters and observations, using a ternary mask to indicate observed, generated, or absent coordinates. This mask-based approach allows any conditional or marginal inference to be performed by simply changing the mask, avoiding the quadratic attention cost of transformer-based generalists. Experiments on high‑dimensional image denoising and shallow water inference show that OneFlowSBI achieves lower posterior error, faster sampling, and can handle missing data without retraining, while matching or exceeding neural posterior estimation on most SBIBM tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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