We present NPE-PFN, a method that uses TabPFN for training-free and simulation-efficient Bayesian inference.

We propose a multi-fidelity simulation-based inference method based on multilevel Monte Carlo for computationally costly simulators.

We propose a method for performing simulation-based inference (SBI) of function-valued parameters, and apply it to spatial inference problems in the geosciences.