Bayesian statistical methods von reich brian (39 Ergebnisse)
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2019
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2019
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Sprache: Englisch
Verlag: CRC Press LLC, 2019
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Sprache: Englisch
Verlag: Routledge, 2021
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 59 von 59. Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: Taylor & Francis Ltd, London, 2021
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 59 von 59. Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Paperback. Zustand: 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 mod…els (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.
Sprache: Englisch
Verlag: Routledge, 2021
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 59 von 59. Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Zustand: New. 2021. 1st Edition. paperback. . . . . .
Sprache: Englisch
Verlag: Routledge, 2021
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 59 von 59. Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
Verlag: Taylor & Francis Ltd, London, 2021
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 59 von 59. Buch 59 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Paperback. Zustand: 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 mod…els (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.
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 112 von 59. Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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hardcover. Zustand: Good. May contain some writing/highlighting. Cover shows wear from normal use. Supplemental materials such as CDs or access codes are not included unless otherwise noted.
Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 112 von 59. 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
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Sprache: Englisch
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 2026
Serie: Chapman & Hall/CRC Texts in Statistical Science, Buch 112 von 59. Buch 112 von 59 - Chapman & Hall/CRC Texts in Statistical Science
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Sprache: Englisch
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Sprache: Englisch
Verlag: Chapman and Hall/CRC, 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: Chapman and Hall/CRC, 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.
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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: Chapman & Hall, 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.
