Description
Bayesian Data Analysis (3rd Ed.)
Chapman & Hall/CRC Texts in Statistical Science Series
Authors: Gelman Andrew, Carlin John B., Stern Hal S., Dunson David B., Vehtari Aki, Rubin Donald B.
Language: EnglishSubject for Bayesian Data Analysis:
Keywords
Posterior Distribution; Prior Distribution; Bayesian Inference In Practice; Posterior Predictive Distribution; Applied Approach To Analysis Using Bayesian Methods; Noninformative Prior Distribution; Variational Bayes; Gibbs Sampler; Bayesian Inference Starting From First Principles; Posterior Density; Hamiltonian Monte Carlo; Conditional Posterior Distribution; Convergence Monitoring And Effective Sample Size Calculations For Iterative Simulation; Posterior Predictive Checks; Cross-Validation And Predictive Information Criteria; Log Posterior Density; Marginal Posterior Density; Marginal Posterior Distribution; Hierarchical Normal Model; Markov Chain Simulation; Uniform Prior Distribution; Prior Density; Gaussian Process Priors; Missing Data; Missing Data Mechanism; HMC; Test Quantities; Metropolis Algorithm; Em Algorithm; MCMC Algorithm; Conditional Posterior; Conjugate Prior Distribution
96.92 €
In Print (Delivery period: 15 days).
Add to cart the book of Gelman Andrew, Carlin John B., Stern Hal S., Dunson David B., Vehtari Aki, Rubin Donald B.667 p. · 17.8x25.4 cm · Hardback
Description
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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.
Fundamentals of Bayesian Inference. Fundamentals of Bayesian Data Analysis. Advanced Computation. Regression Models. Nonlinear and Nonparametric Models. Appendices.