To see accurate pricing, please choose your delivery country.
United States
All Shops

British Wildlife

8 issues per year 84 pages per issue Subscription only

British Wildlife is the leading natural history magazine in the UK, providing essential reading for both enthusiast and professional naturalists and wildlife conservationists. Published eight times a year, British Wildlife bridges the gap between popular writing and scientific literature through a combination of long-form articles, regular columns and reports, book reviews and letters.

Subscriptions from £33 per year

Conservation Land Management

4 issues per year 44 pages per issue Subscription only

Conservation Land Management (CLM) is a quarterly magazine that is widely regarded as essential reading for all who are involved in land management for nature conservation, across the British Isles. CLM includes long-form articles, events listings, publication reviews, new product information and updates, reports of conferences and letters.

Subscriptions from £26 per year
Academic & Professional Books  Reference  Data Analysis & Modelling  Data Analysis & Statistics

Bayesian Data Analysis

By: Andrew Gelman(Author), John B Carlin(Author), Hal S Stern(Author), David B Dunson(Author), Aki Vehtari(Author), Donald B Rubin(Author)
661 pages, 121 b/w illustrations, 49 tables
Bayesian Data Analysis
Click to have a closer look
  • Bayesian Data Analysis ISBN: 9781439840955 Edition: 3 Hardback Nov 2013 Not in stock: Usually dispatched within 1 week
Price: £79.99
About this book Contents Customer reviews Related titles

About this book

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

Bayesian Data Analysis 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 Bayesian Data Analysis's web page.


Probability and Inference
Single-Parameter Models
Introduction to Multiparameter Models
Asymptotics and Connections to Non-Bayesian Approaches
Hierarchical Models

Model Checking
Evaluating, Comparing, and Expanding Models
Modeling Accounting for Data Collection
Decision Analysis

Introduction to Bayesian Computation
Basics of Markov Chain Simulation
Computationally Efficient Markov Chain Simulation
Modal and Distributional Approximations

Introduction to Regression Models
Hierarchical Linear Models
Generalized Linear Models
Models for Robust Inference 
Models for Missing Data

Parametric Nonlinear Models
Basic Function Models
Gaussian Process Models
Finite Mixture Models
Dirichlet Process Models

A: Standard Probability Distributions
B: Outline of Proofs of Asymptotic Theorems
C: Computation in R and Stan

Bibliographic Notes and Exercises appear at the end of each chapter.

Customer Reviews

By: Andrew Gelman(Author), John B Carlin(Author), Hal S Stern(Author), David B Dunson(Author), Aki Vehtari(Author), Donald B Rubin(Author)
661 pages, 121 b/w illustrations, 49 tables
Media reviews

"The second edition was reviewed in JASA by Maiti (2004) [...] we now stand 10 years later with an even more impressive textbook that truly stands for what Bayesian data analysis should be. [...] this being a third edition begets the question of what is new when compared with the second edition? Quite a lot [...] this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis."
– Christian P. Robert, Journal of the American Statistical Association, September 2014, Vol. 109

Praise for the Second Edition:
"[...] it is simply the best all-around modern book focused on data analysis currently available. [...] There is enough important additional material here that those with the first edition should seriously consider updating to the new version. [...] when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995. The second edition makes this an even more robust choice."
– Lawrence Joseph, Montreal General Hospital and McGill University, Statistics in Medicine, Vol. 23, 2004

"I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems."
– John Grego, University of South Carolina, USA

"[...] easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods"
– David Blackwell, University of California, Berkeley, USA

Current promotions
New and Forthcoming BooksNHBS Moth TrapBritish Wildlife MagazineBuyers Guides