Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

English | 2023 | ISBN: 978-1032234960 | 288 Pages | PDF | 29 MB

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. 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

The book 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.

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