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

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.

基於模型的聚類和分類方法提供了一種系統性的統計方法，通過混合建模來進行聚類、分類和密度估計。基於模型的框架使得在統計建模的背景下理解選擇或開發方法的問題成為可能。統計環境R的mclust套件是一個廣泛使用的平台，實現了這些基於模型的策略。該套件包括摘要和可視化功能，以補充模型的估計和選擇過程。

本書的主要特點包括：
- 對基於模型的方法和mclust R套件的介紹
- 對mclust和底層建模策略的詳細描述
- 大量的例子、彩色圖表和圖像，以及重現它們的R代碼
- 附帶網站的支持，包括重現書中例子和圖像的R代碼、勘誤表和其他補充資料

本書適合具有基本統計方法（包括推斷和計算）的量化訓練的學生和研究人員閱讀。除了作為mclust的參考手冊外，本書對於希望在統計學、數據科學、臨床研究、社會科學和其他許多學科中應用這些基於模型的技術的人士尤其有用。

Luca Scrucca
Associate 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).

Luca Scrucca
是意大利佩魯賈大學統計學副教授，他的研究興趣包括：混合模型、基於模型的聚類和分類、統計學習、降維方法、遺傳和演化算法。他目前是《統計軟件期刊》和《統計與計算》的副編輯。他開發並維護了幾個在《綜合R存檔網絡》（CRAN）上可用的知名R軟件包。

Chris Fraley最近是Tableau的首席研究員，之前在華盛頓大學和Insightful擔任統計學研究職位。她在應用統計學的多個領域貢獻了計算方法，並且是幾個廣泛使用的R軟件包的主要作者。她是數值函數（如nlminb）的創始人，這些函數在R核心統計軟件包中長期可用。

T. Brendan Murphy是愛爾蘭都柏林大學統計學教授，他的研究興趣包括：基於模型的聚類、分類、網絡建模和潛變量建模。他對社會科學、政治科學、醫學、食品科學和生物學的應用感興趣。他曾擔任《統計與計算》期刊的副編輯，目前是《應用統計學年鑑》的編輯和《統計分析與數據挖掘》的副編輯。

Adrian Raftery
是美國華盛頓大學的波音國際統計學和社會學教授，也是大氣科學的兼職教授。他還是華盛頓大學統計和社會科學中心以及人口學和生態學研究中心的教職員。他是基於模型的聚類的創始研究人員之一，自1984年以來在該領域發表了多篇論文。他的研究興趣包括：基於模型的聚類、貝葉斯統計、社會網絡分析和統計人口學。他對社會、環境、生物和健康科學的應用感興趣。他是美國國家科學院的成員，被湯姆森路透評為1995年至2005年間世界上引用最多的數學研究人員。他曾擔任《美國統計協會期刊》的編輯。