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Bayesian Data Analysis | 3:e upplagan
- Danskt band, Engelska, 2013
- Författare: Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
- Betyg:
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Beskrivning
Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.
New to the Third Edition
Four new chapters on nonparametric modeling
Coverage of weakly informative priors and boundary-avoiding priors
Updated discussion of cross-validation and predictive information criteria
Improved convergence monitoring and effective sample size calculations for iterative simulation
Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
New and revised software code
The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.
New to the Third Edition
Four new chapters on nonparametric modeling
Coverage of weakly informative priors and boundary-avoiding priors
Updated discussion of cross-validation and predictive information criteria
Improved convergence monitoring and effective sample size calculations for iterative simulation
Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
New and revised software code
The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Om denna bok
Bayesian Data Analysis av Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari och Donald B. Rubin är en Danskt band bok med 675 sidor på Engelska. Detta är den 3:e upplagan som utgavs 2013 av Taylor & Francis Ltd.
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Används vid svenska universitet
Denna kurslitteratur används aktivt i 4 universitetskurser vid lärosäten runt om i Sverige, inklusive:
Produktinformation
Kategori:
Okänd
Bandtyp:
Danskt band
Språk:
Engelska
ISBN:
9781439840955
Upplaga:
3
Utgiven:
2013-11-05
Förlag:
Taylor & Francis Ltd
Sidantal:
675