Bayesian Statistical Methods by Reich Brian (34 results)
Language: English
Published by Chapman and Hall/CRC, 2019
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
Language: English
Published by Chapman and Hall/CRC, 2019
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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hardcover. Condition: Acceptable. Readable condition, all page intact, has wear, some writing or highlighting inside.
Language: English
Published by CRC Press LLC, 2019
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Condition: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
Language: English
Published by Routledge, 2021
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by CRC Press, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by CRC Press, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by CRC Press, 2019
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Condition: 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.
Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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hardcover. Condition: New.
Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Condition: New. 2026. 2nd Edition. hardcover. . . . . .
Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Language: English
Published by Taylor & Francis Ltd, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Hardback. Condition: New. New copy - Usually dispatched within 4 working days.
Language: English
Published by Taylor and Francis Ltd, GB, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Hardback. Condition: 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 r…egression, 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.
Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Language: English
Published by Taylor and Francis Ltd, GB, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Hardback. Condition: 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 r…egression, 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.
Language: English
Published by Chapman and Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Condition: New. 2026. 2nd Edition. hardcover. . . . . . Books ship from the US and Ireland.
Language: English
Published by CRC Press, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Condition: 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.
Language: English
Published by Taylor and Francis Ltd, GB, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
Seller: Rarewaves USA United, HEBRON, KY, U.S.A.Rarewaves USA United
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Hardback. Condition: 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 r…egression, 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.
Language: English
Published by Chapman & Hall, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Hardcover. Condition: Brand New. 2nd edition. 360 pages. 10.00x7.00x10.24 inches. In Stock.
Language: English
Published by Taylor and Francis Ltd, GB, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Hardback. Condition: 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 r…egression, 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.
Language: English
Published by CRC Press, 2021
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Softcover
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Condition: 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… & Elva M.
Language: English
Published by CRC Press, 2019
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
- Print on Demand
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Gebunden. Condition: 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 practic…e including multiple linear re.
Language: English
Published by Taylor & Francis Ltd, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Hardcover. Condition: 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 multi…ple 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.
Language: English
Published by Chapman And Hall/CRC, 2026
Series: Book 112 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
- Print on Demand
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Buch. Condition: 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 regress…ion, mixed effects models and generalized linear models.
