Bayesian Statistical Methods by Reich Brian (37 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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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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Language: English
Published by CRC Press LLC, 2019
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Language: English
Published by Taylor & Francis Ltd, London, 2021
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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Paperback. Condition: new. Paperback. 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 regression, mixed effects models and generalized linear m…odels (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis 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 are available on the books website.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 Martin Teaching Award.Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute. Designed to provide a good balance of theory and computational methods that will appeal to students and practitioners with minimal mathematical and statistical background and no experience in Bayesian statistics to students and practitioners looking for advanced methodologies. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by Routledge, 2021
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
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paperback. Condition: New. 1st Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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 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, 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 CRC Press, 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
- Hardcover
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Language: English
Published by Taylor & Francis Ltd, London, 2021
Series: Book 59 of 59 - Chapman & Hall/CRC Texts in Statistical Science
- Softcover
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Paperback. Condition: new. Paperback. 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 regression, mixed effects models and generalized linear m…odels (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis 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 are available on the books website.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 Martin Teaching Award.Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute. Designed to provide a good balance of theory and computational methods that will appeal to students and practitioners with minimal mathematical and statistical background and no experience in Bayesian statistics to students and practitioners looking for advanced methodologies. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, 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
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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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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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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
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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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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, OSWEGO, IL, U.S.A.Rarewaves 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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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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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
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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
- Print on Demand
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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.
