Speaker
Description
I will introduce introduce Rapid Simulation Based Inference (RSBI), a diffusion-based variational approach to likelihood-free Bayesian inference that achieves high sampling efficiency under large prior-to-posterior volumes with multi-modal posterior structure.
RSBI builds on advances in Schrödinger Bridge diffusion sampling to handle multi-modal posteriors, with the likelihood initially supplied by a surrogate distance measure via a differentiable simulator, followed by neural ratio estimator trained on a constrained corpus of simulation/parameter pairs. The key innovation is that an appropriate surrogate likelihood gives a highly efficient proposal distribution for multi-modal posteriors, replacing sequential rounds of other methods with a one-shot proposal. Subsequent NRE refinement targets the posterior under the simulator and original model prior.
As a variational method, RSBI does not suffer from leakage and implicit target drift commonly observed in standard sequential posterior estimation methods. To improve mode coverage we optionally utilize the well-tempered meta-dynamics framework, which also encourages exploration of the prior volume. We observe strong performance on standard SBI benchmarks, particularly when the prior volume is scaled up to 2500 times the original, achieving a performance degradation of only $\sim5\%$ on Two Moons under a highly constrained simulation budget. Additionally, we evaluate RSBI's performance for gravitational wave ring-down posterior estimation, in a real-world physics benchmark inspired by black hole spectroscopy.