Bayesian Generative Model

Bayesian generative models are probabilistic frameworks used to learn complex data distributions, aiming to generate new data similar to the observed data and quantify uncertainty in predictions. Current research emphasizes developing scalable inference methods, such as Markov Chain Monte Carlo (MCMC) techniques enhanced by variational autoencoders (VAEs), and applying these models to diverse high-dimensional data, including multi-omics data and medical images. These models are proving valuable in various fields, enabling improved data analysis in healthcare (e.g., identifying disease subtypes), biological sequence analysis (e.g., evolutionary parameter estimation), and other areas requiring robust uncertainty quantification and data generation.