Bayesian Methods and Mixture Models

Bayesian Methods and Mixture Models is a research topic within Artificial Intelligence. Science Explorer counts 25k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of mixture models, particularly Gaussian finite mixture models and Dirichlet process mixture models, for model-based clustering, discriminant analysis, density estimation, and unsupervised learning. It explores various inference methods such as Bayesian inference, variational inference, and Markov Chain Monte Carlo for estimating parameters in mixture models. The cluster also delves into the challenges of identifiability, variable selection, and dealing with label switching in the context of mixture models.

• Mixture Models
• Clustering
• Bayesian Inference
• Dirichlet Process
• Gaussian Mixture Models
• Variational Inference
• Markov Chain Monte Carlo
• Finite Mixtures
• Hidden Markov Models
• Nonparametric Bayesian