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Handbuch des Bayesian, Fiducial und - Hardcover, von Berger James; Meng - gut-
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eBay-Artikelnr.:405615202719
Artikelmerkmale
- Artikelzustand
- Book Title
- Handbook of Bayesian, Fiducial, and Frequentist Inference (Chapma
- ISBN
- 9780367321987
Über dieses Produkt
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
036732198X
ISBN-13
9780367321987
eBay Product ID (ePID)
17062268931
Product Key Features
Number of Pages
406 Pages
Publication Name
Handbook of Bayesian, Fiducial, and Frequentist Inference
Language
English
Publication Year
2024
Subject
Probability & Statistics / General, General
Type
Textbook
Subject Area
Mathematics
Series
Chapman and Hall/Crc Handbooks of Modern Statistical Methods Ser.
Format
Hardcover
Dimensions
Item Weight
34.7 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2023-037656
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
519.542
Table Of Content
1. Risky Business 2. Empirical Bayes: Concepts and Methods 3. Distributions for Parameters 4. Objective Bayesian Inference and its Relationship to Frequentism 5. Fiducial Inference, Then and Now 6. Bridging Bayesian, frequentist and fiducial inferences using confidence distributions 7. Objective Bayesian Testing and Model Uncertainty 8. "A BFFer's Exploration with Nuisance Constructs: Bayesian p-value, H likelihood, and Cauchyanity" 9. Bayesian neural networks and dimensionality reduction 10. The Tangent Exponential Model 11. Data Integration and Model Fusion in the Bayesian and Frequentist Frameworks 12. How the game-theoretic foundation for probability resolves the Bayesian vs. frequentist standoff 13. "Introduction to Generalized Fiducial Inference" 14. "Dempster-Shafer Theory for Statistical Inference" 15. Slicing and Dicing a Path Through the Fiducial Forest 16. Inferential models and possibility measures 17. Conformal predictive distributions: an approach to nonparametric ducial prediction 18. Fiducial Inference and Decision Theory Index
Synopsis
This handbook provides a comprehensive introduction and overview of the key developments in the BFF schools of inference. It is intended to provide researchers and students an overview of foundations of inference from the BFF perspective and provides a general reference for BFF inference., 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 inference Gives an overview of modern inferential methods, allowing scientists in other fields to expand their knowledge Is accessible for readers with different perspectives and backgrounds
LC Classification Number
QA279.4.H3 2024
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