Research Paper#Simulation-Based Inference, Diffusion Models, Machine Learning, Scientific Computing🔬 ResearchAnalyzed: Jan 3, 2026 16:31
Diffusion-based Simulation-Based Inference: A Review
Published:Dec 26, 2025 18:18
•1 min read
•ArXiv
Analysis
This paper provides a comprehensive review of diffusion-based Simulation-Based Inference (SBI), a method for inferring parameters in complex simulation problems where likelihood functions are intractable. It highlights the advantages of diffusion models in addressing limitations of other SBI techniques like normalizing flows, particularly in handling non-ideal data scenarios common in scientific applications. The review's focus on robustness, addressing issues like misspecification, unstructured data, and missingness, makes it valuable for researchers working with real-world scientific data. The paper's emphasis on foundations, practical applications, and open problems, especially in the context of uncertainty quantification for geophysical models, positions it as a significant contribution to the field.
Key Takeaways
- •Reviews diffusion-based SBI, a method for likelihood-free inference.
- •Highlights the use of diffusion models for posterior sampling.
- •Addresses robustness in non-ideal data scenarios (misspecification, unstructured data, missingness).
- •Discusses open problems and applications in uncertainty quantification for geophysical models.
Reference
“Diffusion models offer a flexible framework for SBI tasks, addressing pain points of normalizing flows and offering robustness in non-ideal data conditions.”