Tokenised Flow Matching for Hierarchical Simulation Based Inference

• URL: http://arxiv.org/abs/2604.20723v1
• Date: Wed, 22 Apr 2026 16:07:47 GMT
• Title: Tokenised Flow Matching for Hierarchical Simulation Based Inference
• Abstract summary: Cost of simulator evaluations is a key practical bottleneck for Simulation Based Inference.<n>In hierarchical settings with shared global parameters and exchangeable site-level parameters and observations, this structure can be exploited to improve simulation efficiency.<n>We propose Tokenised Flow Matching for Posterior Estimation (TFMPE), a tokenised flow matching approach that supports function-valued observations through likelihood factorisation.
• Score: 0.4199844472131922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The cost of simulator evaluations is a key practical bottleneck for Simulation Based Inference (SBI). In hierarchical settings with shared global parameters and exchangeable site-level parameters and observations, this structure can be exploited to improve simulation efficiency. Existing hierarchical SBI approaches factorise the posterior yet still simulate across multiple sites per training sample; We instead explore likelihood factorisation (LF) to train from single-site simulations. In LF sampling we learn a per-site neural surrogate of the simulator and then assemble synthetic multi-site observations to amortise inference for the full hierarchical posterior. Building on this, we propose Tokenised Flow Matching for Posterior Estimation (TFMPE), a tokenised flow matching approach that supports function-valued observations through likelihood factorisation. To enable systematic evaluation, we introduce a benchmark for hierarchical SBI. We validate TFMPE on this benchmark and on realistic infectious disease and computational fluid dynamics models, finding well-calibrated posteriors while reducing computational cost.

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