Bayesian statistical methods von reich brian (34 Ergebnisse)
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2019
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2019
Serie: Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: CRC Press LLC, 2019
Serie: Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: Routledge, 2021
Serie: Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
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Sprache: Englisch
Verlag: CRC Press, 2026
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
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Sprache: Englisch
Verlag: CRC Press, 2026
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Sprache: Englisch
Verlag: CRC Press, 2019
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Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:9780815378648.
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Zustand: New. 2026. 2nd Edition. hardcover. . . . . .
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Sprache: Englisch
Verlag: Taylor & Francis Ltd, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Hardback. Zustand: New. Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear reg…ression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website.
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Hardback. Zustand: New. Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear reg…ression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website.
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Zustand: New. 2026. 2nd Edition. hardcover. . . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: CRC Press, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Zustand: New. Brian J. Reich, Gertrude M. Cox Distinguished Professor of Statistics at North Carolina State University, applies Bayesian statistical methods in a variety of fields including environmental epidemiology, engineering, weather and climate.
Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Hardback. Zustand: New. Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear reg…ression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website.
Sprache: Englisch
Verlag: Chapman & Hall, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2026
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Hardback. Zustand: New. Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multiple linear reg…ression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the book's website.
Sprache: Englisch
Verlag: CRC Press, 2021
Serie: Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Softcover
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy &…amp Elva M.
Sprache: Englisch
Verlag: CRC Press, 2019
Serie: Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
- Print-on-Demand
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Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice…including multiple linear re.
Sprache: Englisch
Verlag: Taylor & Francis Ltd, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
- Print-on-Demand
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Hardcover. Zustand: new. Hardcover. Bayesian Statistical Methods: With Applications to Machine Learning provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, this book is more focused on Bayesian methods applied routinely in practice, including multipl…e linear regression, mixed effects models and generalized linear models. This second edition includes a new chapter on Bayesian machine learning methods to handle large and complex datasets and several new applications to illustrate the benefits of the Bayesian approach in terms of uncertainty quantification. Readers familiar with only introductory statistics will find this book accessible, as it includes many worked examples with complete R code, and comparisons are presented with analogous frequentist procedures. The book can be used as a one-semester course for advanced undergraduate and graduate students and can be used in courses comprising undergraduate statistics majors, as well as non-statistics graduate students from other disciplines such as engineering, ecology and psychology. In addition to thorough treatment of the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC) samplingModel-comparison and goodness-of-fit measures, including sensitivity to priors.To illustrate the flexibility of the Bayesian approaches for complex data structures, the latter chapters provide case studies covering advanced topics:Handling of missing and censored dataPriors for high-dimensional regression modelsMachine learning models including Bayesian adaptive regression trees and deep learningComputational techniques for large datasetsFrequentist properties of Bayesian methods.The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets and complete data analyses is made available on the books website. This book provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, it is more focused on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Sprache: Englisch
Verlag: Chapman And Hall/CRC, 2026
Serie: Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, it is more focused on Bayesian methods applied routinely in practice including multiple linear regressio…n, mixed effects models and generalized linear models.
