{"product_id":"model-based-clustering-classification-and-density-estimation-using-mclust-in-r-paperback","title":"Model-Based Clustering, Classification, and Density Estimation Using McLust in R - Paperback","description":"\u003cp\u003eby \u003cb\u003eLuca Scrucca\u003c\/b\u003e (Author), \u003cb\u003eChris Fraley\u003c\/b\u003e (Author), \u003cb\u003eT. Brendan Murphy\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eModel-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 an appropriate clustering or classification method to be understood within the context of statistical modeling. The \u003cb\u003emclust\u003c\/b\u003e package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey features of the book: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eAn introduction to the model-based approach and the \u003cb\u003emclust\u003c\/b\u003e R package\u003c\/li\u003e \u003cli\u003eA detailed description of \u003cstrong\u003emclust\u003c\/strong\u003e and the underlying modeling strategies\u003c\/li\u003e \u003cli\u003eAn extensive set of examples, color plots, and figures along with the R code for reproducing them\u003c\/li\u003e \u003cli\u003eSupported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eModel-Based Clustering, Classification, and Density Estimation Using mclust in R \u003c\/strong\u003eis accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for \u003cb\u003emclust\u003c\/b\u003e, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eLuca Scrucca\u003c\/strong\u003e\u003cbr\u003eAssociate 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). \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cstrong\u003eChris Fraley\u003c\/strong\u003eMost 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. \u003cp\u003e\u003c\/p\u003e\u003cstrong\u003eT. Brendan Murphy\u003c\/strong\u003eProfessor 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. \u003cp\u003e\u003c\/p\u003e\u003cstrong\u003eAdrian Raftery\u003c\/strong\u003e\u003cbr\u003eBoeing 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). \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 242\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.57 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e April 20, 2023\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42719823921215,"sku":"9781032234953","price":151.61,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/ee9d8120f22f47bc12be58e16d245931.webp?v=1765087010","url":"https:\/\/dhlswag.com\/products\/model-based-clustering-classification-and-density-estimation-using-mclust-in-r-paperback","provider":"BBB","version":"1.0","type":"link"}