Model Based Clustering Classification And Density Estimation Using Mclust In R
Download Model Based Clustering Classification And Density Estimation Using Mclust In R PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Model Based Clustering Classification And Density Estimation Using Mclust In R book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
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 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.
Finite Mixture Models
Author: Geoffrey McLachlan
language: en
Publisher: John Wiley & Sons
Release Date: 2004-03-22
An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Drug Development Supported by Informatics
This book describes the state-of-the-art of chemoinformatics, bioinformatics, materials informatics and measurement/metrology informatics to develop drugs with desired activity or physicochemical properties and to optimize the functionality, efficacy, safety and quality of the compounds for drugs. Recently, "AI drug discovery", drug discovery research utilizing artificial intelligence technology such as machine learning, has attracted much attention. This book provides an overview of the four applied informatics fields and their applications in drug development for a wide spectrum of readers from learners to professional scientists in academia and industry. It focuses on the basic research stage of drug development with contributions from experts at the forefront of these fields. The authors hope that this book will be of assistance to explore new opportunities for collaboration between pharmaceutical science and informatics.