Bayesian Data Analysis (Chapman & Hall / CRC Texts in Statistical Science)

Gelman, Professor In The Department Of Statistics Andrew; Carlin, John B; Stern, Hal S

ISBN 10: 1439840954 / ISBN 13: 9781439840955

Verlag: Chapman & Hall, 2013

Condition: Excellent (See the picture. Just a small bump on the left-down corner of the back cover. Not important).

"Bayesian Data Analysis, Third Edition" is the leading graduate textbook on applied Bayesian inference and statistical modeling, offering an accessible and practical approach to real-world data analysis problems.

This comprehensive reference covers fundamental concepts like prior and posterior distributions and hierarchical models, and then progresses to advanced computational methods including Hamiltonian Monte Carlo (HMC) using Stan and Markov Chain Monte Carlo (MCMC) simulation.

This edition features expanded content on nonparametric Bayesian modeling techniques such as Gaussian processes and Dirichlet process mixtures, along with updated discussions on Bayesian model checking and comparison using cross-validation and predictive information criteria. It's an essential resource for statistics students and researchers seeking to perform modern applied Bayesian statistics with up-to-date software code. Bestandsnummer des Verkäufers ABE-1760355457420

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor: Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Doanld B. Rubin

Bibliografische Details

Titel: Bayesian Data Analysis (Chapman & Hall / CRC...
Verlag: Chapman & Hall
Erscheinungsdatum: 2013
Einband: Hardcover
Zustand: Sehr gut
Auflage: 3. Auflage