Gilles Celeux – författare & böcker

Handbook of Mixture Analysis

AvGilles Celeux,Christian P. Robertm. fl.

Häftad, Engelska, 2020

932 kr

Skickas inom 10-15 vardagar

Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.Features:Provides a comprehensive overview of the methods and applications of mixture modelling and analysisDivided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected ApplicationsContains many worked examples using real data, together with computational implementation, to illustrate the methods describedIncludes contributions from the leading researchers in the fieldThe Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Handbook of Mixture Analysis

AvChristian P. Robert,Gilles Celeuxm. fl.

E-bok

PDF, Engelska, 2019

1 071 kr

Läs direkt efter köp

Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time.

The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.

Features:

Provides a comprehensive overview of the methods and applications of mixture modelling and analysis

Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications

Contains many worked examples using real data, together with computational implementation, to illustrate the methods described

Includes contributions from the leading researchers in the field

The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Handbook of Mixture Analysis

AvChristian P. Robert,Gilles Celeuxm. fl.

E-bok

Engelska, 2019

1 071 kr

Läs direkt efter köp

Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time.

The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.

Features:

Provides a comprehensive overview of the methods and applications of mixture modelling and analysis

Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications

Contains many worked examples using real data, together with computational implementation, to illustrate the methods described

Includes contributions from the leading researchers in the field

The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Del 50 - Cambridge Series in Statistical and Probabilistic Mathematics

Model-Based Clustering and Classification for Data Science

With Applications in R

AvCharles Bouveyron,Gilles Celeuxm. fl.

Inbunden, Engelska, 2019

1 006 kr

Skickas inom 7-10 vardagar

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.

Model-Based Clustering and Classification for Data Science

With Applications in R

AvAdrian E. Raftery,T. Brendan Murphym. fl.

E-bok

PDF, Engelska, 2019

1 211 kr

Läs direkt efter köp

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.

Handbook of Mixture Analysis

AvSylvia Fruhwirth-Schnatter,Gilles Celeuxm. fl.

Inbunden, Engelska, 2019

2 599 kr

Skickas inom 10-15 vardagar

Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the fieldThe Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

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Gilles Celeux – författare

Handbook of Mixture Analysis

Handbook of Mixture Analysis

Handbook of Mixture Analysis

Model-Based Clustering and Classification for Data Science

Model-Based Clustering and Classification for Data Science

Handbook of Mixture Analysis

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