Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
autoregressive conditional density estimatorsbayesian inferencecontext size limithyperparameter tuningmodel misspecificationneural posterior estimationposterior distributionsprior-data fitted networksprobabilistic foundation modelssimulation efficiencysimulation-based inferencestochastic inverse problemssynthetic datatabular data
Simulation-based inference (SBI) offers a flexible and general approach to performing Bayesian inference: In SBI, a neural network is trained on synthetic data simulated from a model and used to rapidly infer posterior distributions for observed data. A key goal for SBI is to achieve accurate inference with as few simulations as possible, especially for expensive simulators. In this work, we address this challenge by repurposing recent probabilistic foundation models for tabular data: We show how tabular foundation models