<ul><li>Simulation-based inference (SBI) uses neural networks to rapidly infer posterior distributions for observed data.</li><li>Tabular foundation models, such as TabPFN, can be used as pre-trained autoregressive conditional density estimators for SBI.</li><li>Neural Posterior Estimation with Prior-data Fitted Networks (NPE-PF) is competitive in terms of accuracy and simulation efficiency.</li><li>NPE-PF eliminates the need for inference network selection, training, and hyperparameter tuning.</li></ul>

Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models

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