Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing methods to be understood within the context of statistical modeling.
Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing methods to be understood within the context of statistical modeling.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Luca ScruccaAssociate Professor of Statistics at Università degli Studi di Perugia, his research interests include: mixture models, model-based clustering and classification, statistical learning, dimension reduction methods, genetic and evolutionary algorithms. He is currently Associate Editor for the Journal of Statistical Software and Statistics and Computing. He has developed and he is the maintainer of several high profile R packages available on The Comprehensive R Archive Network (CRAN). Chris FraleyMost recently a lead research staff member at Tableau, she previously held research positions in Statistics at the University of Washington and at Insightful from its early days as Statistical Sciences. She has contributed to computational methods in a number of areas of applied statistics, and is the principal author of several widely-used R packages. She was the originator (at Statistical Sciences) of numerical functions such as nlminb that have long been available in the R core stats package. T. Brendan MurphyProfessor of Statistics at University College Dublin, his research interests include: model-based clustering, classification, network modeling and latent variable modeling. He is interested in applications in social science, political science, medicine, food science and biology. He served as Associate Editor for the journal Statistics and Computing, he is currently Editor for the Annals of Applied Statistics and Associate Editor for Statistical Analysis and Data Mining. Adrian Raftery Boeing International Professor of Statistics and Sociology, and Adjunct Professor of Atmospheric Sciences at the University of Washington, Seattle. He is also a faculty affiliate of the Center for Statistics and the Social Sciences and the Center for Studies in Demography and Ecology at University of Washington. He was one of the founding researchers in model-based clustering, having published in the area since 1984. His research interests include: model-based clustering, Bayesian statistics, social network analysis and statistical demography. He is interested in applications in social, environmental, biological and health sciences. He is a member of the U.S. National Academy of Sciences and was identified by Thomson-Reuter as the most cited researcher in mathematics in the world for the decade 1995--2005. He served as Editor of the Journal of the American Statistical Association (JASA).
Inhaltsangabe
1. Introduction. 2. Finite Mixture Models. 3. Model Based Clustering. 4. Mixture based Classification. 5. Model based Density Estimation. 6. Visualizing Gaussian Mixture Models. 7. Miscellanea.
1. Introduction. 2. Finite Mixture Models. 3. Model Based Clustering. 4. Mixture based Classification. 5. Model based Density Estimation. 6. Visualizing Gaussian Mixture Models. 7. Miscellanea.
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