|
|
Product Description
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.
Features
- Used Book in Good Condition
Customers Who Bought This Item Also Bought
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
- A Student’s Guide to Bayesian Statistics
- Data Analysis Using Regression and Multilevel/Hierarchical Models
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
- Causality: Models, Reasoning and Inference
- A First Course in Bayesian Statistical Methods (Springer Texts in Statistics)
- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
*If this is not the "Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)" product you were looking for, you can check the other results by clicking this link
.
Details were last updated on May 8, 2024 10:38 +08.
