9780367321987 - handbook of bayesian, fiducial, and frequentist inference (20 risultati)
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Handbook of Bayesian, Fiducial, and Frequentist Inference
Berger, James (EDT); Meng, Xiao-Li (EDT); Reid, Nancy (EDT); Xie, Min-ge (EDT)
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Handbook of Bayesian, Fiducial, and Frequentist Inference
Berger, James (EDT); Meng, Xiao-Li (EDT); Reid, Nancy (EDT); Xie, Min-ge (EDT)
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Handbook of Bayesian, Fiducial, and Frequentist Inference
Berger, James (EDT); Meng, Xiao-Li (EDT); Reid, Nancy (EDT); Xie, Min-ge (EDT)
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Handbook of Bayesian, Fiducial, and Frequentist Inference
Berger, James (EDT); Meng, Xiao-Li (EDT); Reid, Nancy (EDT); Xie, Min-ge (EDT)
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Handbook of Bayesian, Fiducial, and Frequentist Inference
Berger, James (Editor)/ Meng, Xiao-Li (Editor)/ Reid, Nancy (Editor)/ Xie, Min-ge (Editor)
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 544 pages. 10.00x7.00x1.06 inches. In Stock.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical… science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference.Key Features:Provides a comprehensive introduction to the key developments in the BFF schools of inferenceGives an overview of modern inferential methods, allowing scientists in other fields to expand their knowledgeIs accessible for readers with different perspectives and backgrounds 422 pp. Englisch.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. James Berger, PhD is the Arts and Sciences Distinguished Professor Emeritus of Statistics at Duke University. Dr. Berger received his PhD in mathematics from Cornell University in 1974. Among the awards and honors, D…r. Berger has receive.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical scie…nce is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many publications, and recent vibrant research activities in statistics, applied mathematics, philosophy and other fields of science reflect the importance of this development. The BFF approaches not only bridge foundations and scientific learning, but also facilitate objective and replicable scientific research, and provide scalable computing methodologies for the analysis of big data. Most of the published work typically focusses on a single topic or theme, and the body of work is scattered in different journals. This handbook provides a comprehensive introduction and broad overview of the key developments in the BFF schools of inference. It is intended for researchers and students who wish for an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference.Key Features:Provides a comprehensive introduction to the key developments in the BFF schools of inferenceGives an overview of modern inferential methods, allowing scientists in other fields to expand their knowledgeIs accessible for readers with different perspectives and backgrounds.
