Isbn: 9780367732066 - handbook of mixture analysis (19 risultati)

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Paperback. Condizione: New. 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. …

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Taschenbuch. Condizione: Neu. Handbook of Mixture Analysis | Sylvia Fruhwirth-Schnatter (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Chapman and Hall/CRC | EAN 9780367732066 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Paperback. Condizione: New. 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. …

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 524 pp. Englisch. …

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Sylvia Fruehwirth-Schnatter is Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics, Vienna University of Economics and Business, Austria. She has contributed to research in Bayesian modelling and MCMC . …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. …