Read Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)

Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)

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Description:Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50). To get started finding Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50), you are right to find our website which has a comprehensive collection of manuals listed.
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ISBN: 110849420X

Description: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50). To get started finding Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)

Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)

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