Bayesian statistical methods par reich brian (34 résultats)
Langue : anglais
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Langue : anglais
Edité par Chapman and Hall/CRC, 2019
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Langue : anglais
Edité par CRC Press LLC, 2019
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Langue : anglais
Edité par Routledge, 2021
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paperback. Etat : New. 1st Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Langue : anglais
Edité par Chapman and Hall/CRC, 2026
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Langue : anglais
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Langue : anglais
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Langue : anglais
Edité par CRC Press, 2026
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Langue : anglais
Edité par CRC Press, 2019
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Etat : 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.
Langue : anglais
Edité par Chapman and Hall/CRC, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
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Langue : anglais
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Langue : anglais
Edité par Chapman and Hall/CRC, 2026
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hardcover. Etat : New.
Langue : anglais
Edité par Chapman and Hall/CRC, 2026
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Etat : New. 2026. 2nd Edition. hardcover. . . . . .
Langue : anglais
Edité par Chapman and Hall/CRC, 2026
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Langue : anglais
Edité par Taylor & Francis Ltd, 2026
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Langue : anglais
Edité par Taylor and Francis Ltd, GB, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
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Hardback. Etat : 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 regre…ssion, 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.
Langue : anglais
Edité par Chapman and Hall/CRC, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
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Langue : anglais
Edité par Taylor and Francis Ltd, GB, 2026
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Hardback. Etat : 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 regre…ssion, 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.
Langue : anglais
Edité par Chapman and Hall/CRC, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
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Etat : New. 2026. 2nd Edition. hardcover. . . . . . Books ship from the US and Ireland.
Langue : anglais
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Etat : 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.
Langue : anglais
Edité par Taylor and Francis Ltd, GB, 2026
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- Couverture rigide
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Hardback. Etat : 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 regre…ssion, 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.
Langue : anglais
Edité par Chapman & Hall, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
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Hardcover. Etat : Brand New. 2nd edition. 360 pages. 10.00x7.00x10.24 inches. In Stock.
Langue : anglais
Edité par Taylor and Francis Ltd, GB, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
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Hardback. Etat : 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 regre…ssion, 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.
Langue : anglais
Edité par CRC Press, 2021
Série : Livre 59 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture souple
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Etat : 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 &am…p Elva M.
Langue : anglais
Edité par CRC Press, 2019
Série : Livre 59 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
- impression à la demande
Vendeur : moluna, Greven, Allemagnemoluna
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Gebunden. Etat : 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 in…cluding multiple linear re.
Langue : anglais
Edité par Taylor & Francis Ltd, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
- impression à la demande
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail
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Hardcover. Etat : 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 multiple…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.
Langue : anglais
Edité par Chapman And Hall/CRC, 2026
Série : Livre 112 sur 59 - Chapman & Hall/CRC Texts in Statistical Science
- Couverture rigide
- impression à la demande
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Buch. Etat : 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 regression,… mixed effects models and generalized linear models.
