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Jim Albert

Published by Springer-Verlag New York Inc., United States (2009)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Springer-Verlag New York Inc., United States, 2009. Paperback. Condition: New. 2nd ed. 2009. Language: English . Brand New Book. There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). Seller Inventory # AAU9780387922973

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Jim Albert

Published by Springer-Verlag New York Inc., United States (2009)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Springer-Verlag New York Inc., United States, 2009. Paperback. Condition: New. 2nd ed. 2009. Language: English . Brand New Book. There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). Seller Inventory # AAU9780387922973

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Albert, Jim

Published by Springer-Verlag New York Inc. (2009)

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About this Item: Springer-Verlag New York Inc., 2009. PAP. Condition: New. New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # IQ-9780387922973

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Jim Albert

Published by Springer (2009)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Springer, 2009. Paperback. Condition: Used: Good. Seller Inventory # SONG0387922970

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Jim Albert

Published by Springer (2009)

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About this Item: Springer, 2009. Paperback. Condition: New. 2nd ed. 2009. Seller Inventory # DADAX0387922970

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Jim Albert

Published by Springer New York 2009-05-01, New York (2009)

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About this Item: Springer New York 2009-05-01, New York, 2009. paperback. Condition: New. Seller Inventory # 9780387922973

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Albert, Jim

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About this Item: Springer-Verlag New York Inc., 2009. Condition: New. 2009. 2nd ed. 2009. Paperback. There has been a dramatic growth in the development and application of Bayesian inferential methods. This book introduces Bayesian modeling by the use of computation using the R language. The new edition contains changes in the R code illustrations. Series: Use R! Num Pages: 300 pages, biography. BIC Classification: PBKS; PBT; PBU; PBV; UGK. Category: (P) Professional & Vocational. Dimension: 236 x 155 x 3. Weight in Grams: 436. . . . . . . Seller Inventory # V9780387922973

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Jim Albert

Published by Springer

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About this Item: Springer. Paperback. Condition: New. New copy - Usually dispatched within 2 working days. Seller Inventory # B9780387922973

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Albert, Jim

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About this Item: Springer-Verlag New York Inc. Condition: New. 2009. 2nd ed. 2009. Paperback. There has been a dramatic growth in the development and application of Bayesian inferential methods. This book introduces Bayesian modeling by the use of computation using the R language. The new edition contains changes in the R code illustrations. Series: Use R! Num Pages: 300 pages, biography. BIC Classification: PBKS; PBT; PBU; PBV; UGK. Category: (P) Professional & Vocational. Dimension: 236 x 155 x 3. Weight in Grams: 436. . . . . . Books ship from the US and Ireland. Seller Inventory # V9780387922973

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Jim Albert

Published by Springer (2009)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Condition: New. Depending on your location, this item may ship from the US or UK. Seller Inventory # 97803879229730000000

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Jim Albert

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Condition: Very Good. Ready for quick shipment to any US location by Experienced seller. CDs and Access codes may not be included as is the case with most used books. Seller Inventory # 621361

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Jim Albert

Published by Springer (2009)

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Albert, Jim

Published by Springer-Verlag New York Inc. (2009)

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About this Item: Springer-Verlag New York Inc., 2009. PAP. Condition: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Seller Inventory # BB-9780387922973

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Jim Albert

Published by Springer-Verlag New York Inc. (2009)

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About this Item: Springer-Verlag New York Inc., 2009. PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # IQ-9780387922973

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Albert, Jim

Published by Springer (2016)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Springer, 2016. Paperback. Condition: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Seller Inventory # ria9780387922973_lsuk

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Albert, Jim

Published by Springer (2018)

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About this Item: Springer, 2018. Paperback. Condition: New. Never used! This item is printed on demand. Seller Inventory # 0387922970

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ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Condition: New. Seller Inventory # ria9780387922973_ing

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JIM ALBERT

Published by Springer (2009)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Springer, 2009. Paperback. Condition: NEW. 9780387922973 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. For all enquiries, please contact Herb Tandree Philosophy Books directly - customer service is our primary goal. Seller Inventory # HTANDREE0275248

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Jim Albert

Published by Springer-Verlag Gmbh Jun 2009 (2009)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Springer-Verlag Gmbh Jun 2009, 2009. Taschenbuch. Condition: Neu. Neuware - There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to t very complex models that cannot be t by alternative frequentist methods. To t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). 298 pp. Englisch. Seller Inventory # 9780387922973

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ISBN 10: 0387922970 ISBN 13: 9780387922973

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Jim Albert

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About this Item: Springer. Paperback. Condition: New. 300 pages. Dimensions: 9.1in. x 6.1in. x 0.7in.There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books, andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to t very complex models that cannot be t by alternative frequentist methods. To t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to dene a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9780387922973

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Jim Albert

Published by Springer-Verlag Gmbh Jun 2009 (2009)

ISBN 10: 0387922970 ISBN 13: 9780387922973

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About this Item: Springer-Verlag Gmbh Jun 2009, 2009. Taschenbuch. Condition: Neu. Neuware - There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to t very complex models that cannot be t by alternative frequentist methods. To t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). 298 pp. Englisch. Seller Inventory # 9780387922973

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Jim Albert

Published by Springer-Verlag Gmbh Jun 2009 (2009)

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About this Item: Springer-Verlag Gmbh Jun 2009, 2009. Taschenbuch. Condition: Neu. Neuware - There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to t very complex models that cannot be t by alternative frequentist methods. To t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). 298 pp. Englisch. Seller Inventory # 9780387922973

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Albert, Jim

Published by Springer (2009)

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About this Item: Springer, 2009. Paperback. Condition: New. This item is printed on demand. Seller Inventory # INGM9780387922973

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Albert, Jim

Published by Springer 2009-06-10 (2009)

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Albert, Jim

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Jim Albert

Published by Springer-Verlag Gmbh Mai 2009 (2009)

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About this Item: Springer-Verlag Gmbh Mai 2009, 2009. Taschenbuch. Condition: Neu. Neuware - There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to t very complex models that cannot be t by alternative frequentist methods. To t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN). 298 pp. Englisch. Seller Inventory # 9780387922973

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Albert, Jim

Published by Springer Verlag (2009)

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About this Item: Springer Verlag, 2009. Paperback. Condition: Brand New. 2nd edition. 298 pages. 9.00x6.25x0.75 inches. In Stock. Seller Inventory # x-0387922970

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Jim Albert

Published by Springer (2009)

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About this Item: Springer, 2009. Paperback. Condition: Good. 2nd ed. 2009. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be ex-library. Shipping & Handling by region. Buy with confidence, excellent customer service!. Seller Inventory # 0387922970

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