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A First Course in Bayesian Statistical Methods
E-Book (PDF)
2009 Springer New York; Springer
Auflage: 2009
IX, 271 Seiten
Sprache: English
ISBN: 978-0-387-92407-6
€ 64,15
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Hauptbeschreibung
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material.
Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.
The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Kurztext / Annotation
This compact, self-contained introduction to the theory and application of Bayesian statistical methods is accessible to those with a basic familiarity with probability, yet allows advanced readers to grasp the principles underlying Bayesian theory and method.
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material.
Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.
The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Kurztext / Annotation
This compact, self-contained introduction to the theory and application of Bayesian statistical methods is accessible to those with a basic familiarity with probability, yet allows advanced readers to grasp the principles underlying Bayesian theory and method.
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.