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OverviewFull Product DetailsAuthor: Luca Scrucca , Chris Fraley , T. Brendan Murphy , Adrian E. RafteryPublisher: Taylor & Francis Ltd Imprint: Chapman & Hall/CRC Weight: 0.494kg ISBN: 9781032234953ISBN 10: 1032234954 Pages: 242 Publication Date: 20 April 2023 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. 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.ReviewsThe book gives an excellent introduction to using the R package mclust for mixture modeling with (multivariate) Gaussian distributions as well as covering the supervised and semi-supervised aspects. A thorough introduction to the theoretic concepts is given, the software implementation described in detail and the application shown on many examples. I particularly enjoyed the in-depth discussion of different visualization methods. ~ Bettina Grun, WU (Vienna University of Economics and Business), Austria Cluster analysis, and its sister subjects of density estimation and mixture-model classification, used to be underserved topics in statistical texts. This magisterial book corrects that imbalance and does so comprehensively. ~ David Banks (Duke University) Author InformationLuca 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). Tab Content 6Author Website:Countries AvailableAll regions |