Buch, Englisch, 271 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 435 g
Reihe: Springer Texts in Statistics
Buch, Englisch, 271 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 435 g
Reihe: Springer Texts in Statistics
ISBN: 978-1-4419-2828-3
Verlag: Springer
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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material.
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Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.
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The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Operations Research
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
- Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
Weitere Infos & Material
and examples.- Belief, probability and exchangeability.- One-parameter models.- Monte Carlo approximation.- The normal model.- Posterior approximation with the Gibbs sampler.- The multivariate normal model.- Group comparisons and hierarchical modeling.- Linear regression.- Nonconjugate priors and Metropolis-Hastings algorithms.- Linear and generalized linear mixed effects models.- Latent variable methods for ordinal data.
