Compartir
Model-Based Clustering, Classification, and Density Estimation Using Mclust in r (Chapman & Hall (en Inglés)
Luca Scrucca; Chris Fraley; T. Brendan Murphy; Adrian E. Raftery (Autor)
·
Taylor & Francis Ltd,
· Tapa Dura
Model-Based Clustering, Classification, and Density Estimation Using Mclust in r (Chapman & Hall (en Inglés) - Luca Scrucca; Chris Fraley; T. Brendan Murphy; Adrian E. Raftery
$ 133.00
$ 190.00
Ahorras: $ 57.00
Elige la lista en la que quieres agregar tu producto o crea una nueva lista
✓ Producto agregado correctamente a la lista de deseos.
Ir a Mis ListasSe enviará desde nuestra bodega entre el
Jueves 06 de Junio y el
Lunes 17 de Junio.
Lo recibirás en cualquier lugar de Estados Unidos entre 1 y 3 días hábiles luego del envío.
Reseña del libro "Model-Based Clustering, Classification, and Density Estimation Using Mclust in r (Chapman & Hall (en Inglés)"
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 an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust 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.Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is 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 mclust, 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.
