Published by Routledge (edition 1), 2021
ISBN 10: 1032093188 ISBN 13: 9781032093185
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ISBN 10: 1032093188 ISBN 13: 9781032093185
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Published by H N H International Limited, 2021
ISBN 10: 1032093188 ISBN 13: 9781032093185
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Published by H N H International Limited, 2021
ISBN 10: 1032093188 ISBN 13: 9781032093185
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
Language: English
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Published by Routledge 2021-06-30, 2021
ISBN 10: 1032093188 ISBN 13: 9781032093185
Language: English
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ISBN 10: 0815378645 ISBN 13: 9780815378648
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
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Published by Chapman and Hall/CRC 2019-06-11, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
Language: English
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ISBN 10: 0815378645 ISBN 13: 9780815378648
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
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Published by Taylor & Francis Inc, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
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ISBN 10: 0815378645 ISBN 13: 9780815378648
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
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Published by Chapman and Hall/CRC, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
Language: English
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Published by Taylor & Francis Group, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
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Published by Chapman And Hall/CRC Jun 2021, 2021
ISBN 10: 1032093188 ISBN 13: 9781032093185
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 models (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 distributionsComputational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priorsFrequentist properties of Bayesian methodsCase studies covering advanced topics illustrate the flexibility of the Bayesian approach:Semiparametric regression Handling of missing data using predictive distributionsPriors for high-dimensional regression modelsComputational techniques for large datasetsSpatial data analysisThe 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 book's 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. 288 pp. Englisch.
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Add to basketCondition: 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.
Published by Taylor & Francis Inc, 2019
ISBN 10: 0815378645 ISBN 13: 9780815378648
Language: English
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Published by Chapman And Hall/CRC, 2021
ISBN 10: 1032093188 ISBN 13: 9781032093185
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 models (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 distributionsComputational methods including Markov chain Monte Carlo (MCMC)Model-comparison and goodness-of-fit measures, including sensitivity to priorsFrequentist properties of Bayesian methodsCase studies covering advanced topics illustrate the flexibility of the Bayesian approach:Semiparametric regressionHandling of missing data using predictive distributionsPriors for high-dimensional regression modelsComputational techniques for large datasetsSpatial data analysisThe 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 book's website.