Physical Sciences → Computer Science → Artificial Intelligence
Bayesian Methods and Mixture Models
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.
57.4K works worldwide1.1M citations
Mixture ModelsClusteringBayesian InferenceDirichlet ProcessGaussian Mixture ModelsVariational InferenceMarkov Chain Monte CarloFinite MixturesHidden Markov ModelsNonparametric Bayesian
Journals publishing in this area
2
361h-index
1.41Impact
13.4KArticles
893.4KCitations
5
305h-index
1.51Impact
14.8KArticles
669.2KCitations
6
Journal of the Royal Statistical Society Series B (Statistical Methodology)
ISSN 1369-7412611 articles in this topic
307h-index
1.61Impact
4.2KArticles
739.3KCitations
7.jpg)
500h-index
1.29Impact
12.2KArticles
1.5MCitations
8
329h-index
2.51Impact
6.4KArticles
679.8KCitations
10
247h-index
2.31Impact
3.8KArticles
491.1KCitations
11
12h-index
0.09Impact
1.6KArticles
1.2KCitations