Congdon peter d (37 résultats)

Wraps. Etat : Near Fine. Square 8vo, 263 pp. One wrapper corner lightly bumped. Wayne Hogan (illustrateur).

Etat : As New. Unread book in perfect condition.

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Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

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Paperback / softback. Etat : New. New copy - Usually dispatched within 4 working days.

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Etat : As New. Unread book in perfect condition.

Paperback. Etat : New.

Etat : New. 2nd edition NO-PA16APR2015-KAP.

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Etat : New. In English.

Paperback. Etat : New. An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods.The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples.The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities.Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book's website.…

Paperback. Etat : Brand New. 2nd edition. 580 pages. 10.00x7.00x1.34 inches. In Stock.

Taschenbuch. Etat : Neu. Bayesian Hierarchical Models | With Applications Using R, Second Edition | Peter D. Congdon | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | Chapman and Hall/CRC | EAN 9781032177151 | Verantwortliche Person für die EU: Taylor and Francis, Taylor & Francis Verlag Gmbh, Kaufingerstr 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.…

Hardcover. Etat : New.

Etat : New.

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Paperback. Etat : New. An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods.The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples.The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities.Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book's website.…

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Etat : New. Peter Congdon is Research Professor in Quantitative Geography and Health Statistics at Queen Mary, University of London.An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demo.

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Hardcover. Etat : New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.