Bayesian Analysis of Linear Models (inbunden)

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Format: Inbunden (Hardback)
Språk: Engelska
Antal sidor: 476
Utgivningsdatum: 1984-12-01
Upplaga: illustrated ed
Förlag: CRC Press Inc
Medarbetare: Broemeling
Illustratör/Fotograf: black & white illustrations 1
Illustrationer: 1, black & white illustrations
Volymtitel: Bayesian Analysis of Linear Models
Dimensioner: 235 x 161 x 37 mm
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Antal komponenter: 1
Komponenter: Contains 60 hardbacks
ISBN: 9780824785826

Bayesian Analysis of Linear Models

Name: Bayesian Analysis of Linear Models
Price: 2086.00 SEK
Availability: InStock
Author: Broemeling
ISBN: 9780824785826

Textbooks and Monographs

av Broemeling

Inbunden, Engelska, 1984-12-01

2086

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With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus, this definitive volume containssomething traditional-a review of Bayesian techniques and methods of estimation, hypothesis,testing, and forecasting as applied to the standard populations ... somethinginnovative-a new approach to mixed models and models not generally studied by statisticianssuch as linear dynamic systems and changing parameter models ... and somethingpractical-clear graphs, eary-to-understand examples, end-of-chapter problems, numerousreferences, and a distribution appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of Linear Models is the definitivemonograph for statisticians, econometricians, and engineers. In addition, this text isideal for students in graduate-level courses such as linear models, econometrics, andBayesian inference.

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Broemeling\,

1. Bayesian Inference for the General Linear Model 2. Linear Statistical Models and Bayesian Inference 3. The Traditional Unear Models 4. The Mixed Model 5. Time Series Models 6. Linear Dynamic Systems 7. Structural Change in Linear Models 8. Multivariate Linear Models 9. Looking Ahead