eBook
Model-Based Clustering, Classification, and Density Estimation Using Mclust in R
| Title: | Model-Based Clustering, Classification, and Density Estimation Using Mclust in R |
|---|---|
| Description: | 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. |
| Authors: | Luca Scrucca, Chris Fraley, T. Brendan Murphy, Adrian E. Raftery |
| Resource Type: | eBook. |
| Subjects: | Estimation theory--Data processing, R (Computer program language), Cluster analysis--Data processing, Gaussian distribution--Data processing |
| Categories: | MATHEMATICS / Probability & Statistics / Regression Analysis, BUSINESS & ECONOMICS / Statistics, COMPUTERS / Machine Theory, MATHEMATICS / Probability & Statistics / General |
| Database: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 3581426 RelevancyScore: 969 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 968.509704589844 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Model-Based Clustering, Classification, and Density Estimation Using Mclust in R – Name: Abstract Label: Description Group: Ab Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Luca+Scrucca%22">Luca Scrucca</searchLink><br /><searchLink fieldCode="AR" term="%22Chris+Fraley%22">Chris Fraley</searchLink><br /><searchLink fieldCode="AR" term="%22T%2E+Brendan+Murphy%22">T. Brendan Murphy</searchLink><br /><searchLink fieldCode="AR" term="%22Adrian+E%2E+Raftery%22">Adrian E. Raftery</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Estimation+theory--Data+processing%22">Estimation theory--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22R+%28Computer+program+language%29%22">R (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis--Data+processing%22">Cluster analysis--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+distribution--Data+processing%22">Gaussian distribution--Data processing</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+Regression+Analysis%22">MATHEMATICS / Probability & Statistics / Regression Analysis</searchLink><br /><searchLink fieldCode="ZK" term="%22BUSINESS+%26+ECONOMICS+%2F+Statistics%22">BUSINESS & ECONOMICS / Statistics</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Machine+Theory%22">COMPUTERS / Machine Theory</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 519.5302855133 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Estimation theory--Data processing Type: general – SubjectFull: R (Computer program language) Type: general – SubjectFull: Cluster analysis--Data processing Type: general – SubjectFull: Gaussian distribution--Data processing Type: general Titles: – TitleFull: Model-Based Clustering, Classification, and Density Estimation Using Mclust in R Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Luca Scrucca – PersonEntity: Name: NameFull: Chris Fraley – PersonEntity: Name: NameFull: T. Brendan Murphy – PersonEntity: Name: NameFull: Adrian E. Raftery – PersonEntity: Name: NameFull: Luca Scrucca – PersonEntity: Name: NameFull: Chris Fraley – PersonEntity: Name: NameFull: T. Brendan Murphy – PersonEntity: Name: NameFull: Adrian E. Raftery IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 – D: 11 M: 08 Type: profile Y: 2023 Identifiers: – Type: isbn-print Value: 9781032234953 – Type: isbn-print Value: 9781032234960 – Type: isbn-electronic Value: 9781000868340 – Type: isbn-electronic Value: 9781000868371 – Type: isbn-electronic Value: 9781003277965 Titles: – TitleFull: Model-Based Clustering, Classification, and Density Estimation Using Mclust in R Type: main |
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