Unnormalized Statistical Model

Unnormalized statistical models, lacking explicit normalization constants, pose significant challenges in probabilistic modeling and inference. Current research focuses on developing efficient estimation techniques, such as noise-contrastive estimation and contrastive variational inference, often employing energy-based models and normalizing flows to approximate intractable distributions. These advancements enable improved Bayesian inference, likelihood estimation in simulation-based inference, and applications in diverse fields like change detection and signal processing, offering more robust and scalable solutions for complex data analysis.