Data analysis : a Bayesian tutorial.

By: Sivia, D. S
Contributor(s): Skilling, J. (John)
Material type: TextTextSeries: Oxford science publications: Publisher: Oxford ; New York : Oxford University Press, 2006Edition: 2nd ed. / D.S. Sivia with J. SkillingDescription: 1 online resource (xii, 246 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9780191546709; 0191546704; 9786611341381; 6611341382Subject(s): Bayesian statistical decision theory | Maximum entropy method | Analyse multivariée | Statistique mathématique | Statistique bayésienne | MATHEMATICS -- Probability & Statistics -- Bayesian Analysis | Bayesian statistical decision theory | Maximum entropy methodGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Data analysis.DDC classification: 519.5 LOC classification: QA279.5 | .S55 2006ebOther classification: 62-01 | 62F15 | 62F15. Online resources: Click here to access online
Contents:
PART I: THE ESSENTIALS; 1. The basics; 2. Parameter estimation I; 3. Parameter estimation II; 4. Model selection; 5. Assigning probabilities; PART II: ADVANCED TOPICS; 6. Non-parametric estimation; 7. Experimental design; 8. Least-squares extensions; 9. Nested sampling; 10. Quantification; A. Gaussian integrals; B. Cox's derivation of probability; Bibliography; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; X.
Summary: Focusing on Bayesian methods and maximum entropy, this book shows how a few fundamental rules can be used to tackle a variety of problems in data analysis. Topics covered include reliability analysis, multivariate optimisation, least-squares and maximum likelihood, and more.
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Includes bibliographical references (pages 237-240) and index.

Focusing on Bayesian methods and maximum entropy, this book shows how a few fundamental rules can be used to tackle a variety of problems in data analysis. Topics covered include reliability analysis, multivariate optimisation, least-squares and maximum likelihood, and more.

PART I: THE ESSENTIALS; 1. The basics; 2. Parameter estimation I; 3. Parameter estimation II; 4. Model selection; 5. Assigning probabilities; PART II: ADVANCED TOPICS; 6. Non-parametric estimation; 7. Experimental design; 8. Least-squares extensions; 9. Nested sampling; 10. Quantification; A. Gaussian integrals; B. Cox's derivation of probability; Bibliography; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; X.

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