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Ajouter au panierPaperback. Etat : As New. 1. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Edité par H N H International Limited, 2021
ISBN 10 : 1032093188 ISBN 13 : 9781032093185
Langue: anglais
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Edité par H N H International Limited, 2021
ISBN 10 : 1032093188 ISBN 13 : 9781032093185
Langue: anglais
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 44,50
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Edité par H N H International Limited, 2021
ISBN 10 : 1032093188 ISBN 13 : 9781032093185
Langue: anglais
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 62,08
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Edité par Chapman and Hall/CRC 2021-06, 2021
ISBN 10 : 1032093188 ISBN 13 : 9781032093185
Langue: anglais
Vendeur : Chiron Media, Wallingford, Royaume-Uni
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Ajouter au panierPaperback or Softback. Etat : New. Bayesian Statistical Methods 0.9. Book.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 62,64
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Edité par CRC Press, A Chapman & Hall Book, Boca Raton, 2019
ISBN 10 : 0815378645 ISBN 13 : 9780815378648
Langue: anglais
Vendeur : Second Story Books, ABAA, Rockville, MD, Etats-Unis
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Ajouter au panierHardcover. Octavo, xii, 275 pages. In Good plus condition. Spine is black with white print. Boards in glossy illustrated paper. Text block has ink checks on series page and title page verso. Illustrated: b&w graphs, figures. NOTE: Shelved in Netdesk Column P. 1395241. FP New Rockville Stock.
Vendeur : Books Liquidation, Sacramento, CA, Etats-Unis
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Ajouter au panierEtat : Acceptable. Readable condition, all page intact, has wear, some writing or highlighting inside.
EUR 77,94
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Ajouter au panierEtat : good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
Edité par Taylor & Francis Ltd, London, 2021
ISBN 10 : 1032093188 ISBN 13 : 9781032093185
Langue: anglais
Vendeur : AussieBookSeller, Truganina, VIC, Australie
EUR 73,18
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Ajouter au panierPaperback. Etat : 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 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 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.
Edité par Chapman and Hall/CRC 2019-06-11, 2019
ISBN 10 : 0815378645 ISBN 13 : 9780815378648
Langue: anglais
Vendeur : Chiron Media, Wallingford, Royaume-Uni
EUR 101,57
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Ajouter au panierHardcover. Etat : New.
Vendeur : Textbooks_Source, Columbia, MO, Etats-Unis
Edition originale
EUR 49,97
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Ajouter au panierhardcover. Etat : Good. 1st Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
EUR 102,80
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EUR 114,88
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Ajouter au panierHardback. Etat : New. New copy - Usually dispatched within 4 working days. 520.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 54,82
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Edité par Taylor & Francis Ltd, London, 2021
ISBN 10 : 1032093188 ISBN 13 : 9781032093185
Langue: anglais
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
EUR 58,03
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Ajouter au panierPaperback. Etat : 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 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 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.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 126,41
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Ajouter au panierEtat : New. In.
EUR 94,84
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Ajouter au panierEtat : 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.
EUR 113,26
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EUR 133,99
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Ajouter au panierEtat : New. pp. 274.
EUR 125,69
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Ajouter au panierEtat : good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
EUR 136,63
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Ajouter au panierEtat : New. pp. 274.
EUR 166,49
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Ajouter au panierHardcover. Etat : Brand New. 275 pages. 9.25x6.50x0.75 inches. In Stock.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 112,03
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Ajouter au panierEtat : New.
Vendeur : moluna, Greven, Allemagne
EUR 51,38
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Ajouter au panierEtat : 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 including multiple linear re.
Edité par Chapman And Hall/CRC Jun 2021, 2021
ISBN 10 : 1032093188 ISBN 13 : 9781032093185
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 53,40
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Ajouter au panierTaschenbuch. Etat : 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.