Multimodal Distribution

Multimodal distributions, characterized by multiple distinct peaks or modes, pose significant challenges in various fields, demanding advanced modeling techniques that accurately capture and represent these complex probability distributions. Current research focuses on developing efficient algorithms, such as variational methods, diffusion models, and ensemble-based approaches like annealed importance sampling, to effectively sample and learn from multimodal data, often leveraging generative models and Bayesian frameworks. This research is crucial for improving the accuracy and robustness of applications across diverse domains, including robotics, anomaly detection, and medical image analysis, where multimodal data is prevalent and accurate modeling is essential for reliable decision-making.