Generative diffusion posterior sampling for informative likelihoods
Abstract
Sequential Monte Carlo (SMC) methods have recently shown successful results for conditional sampling of generative diffusion models. In this paper we propose a new diffusion posterior SMC sampler achieving improved statistical efficiencies, particularly under outlier conditions or highly informative likelihoods. The key idea is to construct an observation path that correlates with the diffusion model and to design the sampler to leverage this correlation for more efficient sampling. Empirical results conclude the efficiency.
Fields of Study & Subjects
Markov Chains and Monte Carlo Methods
Gaussian Processes and Bayesian Inference
Adversarial Robustness in Machine Learning
Publication details
- Journal / source
- Communications in Information and Systems
- Volume
- 26
- Issue
- 1
- Pages
- 151–167
- Publication date
- 1 January 2026
- Language
- EN