ConDiSim: Conditional Diffusion Models for Simulation Based Inference

• URL: http://arxiv.org/abs/2505.08403v1
• Date: Tue, 13 May 2025 09:58:23 GMT
• Title: ConDiSim: Conditional Diffusion Models for Simulation Based Inference
• Authors: Mayank Nautiyal, Andreas Hellander, Prashant Singh,
• Abstract summary: ConDiSim is a conditional diffusion model for simulation-based inference of complex systems with intractable likelihoods.<n>It is evaluated across ten benchmark problems and two real-world test problems, where it demonstrates effective posterior approximation accuracy.
• Score: 0.3749861135832073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a conditional diffusion model - ConDiSim, for simulation-based inference of complex systems with intractable likelihoods. ConDiSim leverages denoising diffusion probabilistic models to approximate posterior distributions, consisting of a forward process that adds Gaussian noise to parameters, and a reverse process learning to denoise, conditioned on observed data. This approach effectively captures complex dependencies and multi-modalities within posteriors. ConDiSim is evaluated across ten benchmark problems and two real-world test problems, where it demonstrates effective posterior approximation accuracy while maintaining computational efficiency and stability in model training. ConDiSim offers a robust and extensible framework for simulation-based inference, particularly suitable for parameter inference workflows requiring fast inference methods.

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