Variational Mixture

Variational mixture models are probabilistic frameworks used to represent complex data distributions as a combination of simpler components, primarily aiming to improve the accuracy and efficiency of clustering, prediction, and inference tasks. Current research focuses on developing novel architectures, such as variational Bayesian finite mixture models and variational autoencoders with mixture components, often incorporating techniques like variational inference and normalizing flows to enhance performance and interpretability. These advancements find applications in diverse fields, including biomedical data analysis (e.g., cancer subtyping), autonomous vehicle trajectory prediction, and single-cell RNA sequencing analysis, enabling more robust and insightful data modeling.