Inference-Time Prior Adaptation in Simulation Based Inference via Guided Diffusion Models

Published: 19 Mar 2025, Last Modified: 25 Apr 2025AABI 2025 Workshop TrackEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Diffusion Models, Bayesian Inference
TL;DR: Plug and play priors for inverse problem diffusion models.
Abstract: Amortized simulator-based inference has emerged as a powerful framework for tackling inverse problems and Bayesian inference in many computational sciences by learning the reverse mapping from observed data to parameters. Once trained on many simulated parameter-data pairs, these methods afford parameter inference for any particular dataset, yielding high-quality posterior samples with only one or a few forward passes of a neural network. While amortized methods offer significant advantages in terms of efficiency and reusability across datasets, they are typically constrained by their training conditions -- particularly the prior distribution of parameters used during training. In this paper, we introduce PriorGuide, a technique that enables on-the-fly adaptation to arbitrary priors at inference time for diffusion-based amortized inference methods. Our method allows users to incorporate new information or expert knowledge at runtime without costly retraining.
Submission Number: 15
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