In response to our recent posting of Amazon’s offer of Bayesian Data Analysis 3rd edition at 40% off, some people asked what was in this new edition, with more information beyond the beautiful cover image and the brief paragraph I’d posted earlier.

Here’s the table of contents. The following sections have all-new material:

1.4 New introduction of BDA principles using a simple spell checking example

2.9 Weakly informative prior distributions

5.7 Weakly informative priors for hierarchical variance parameters

7.1-7.4 Predictive accuracy for model evaluation and comparison

10.6 Computing environments

11.4 Split R-hat

11.5 New measure of effective number of simulation draws

13.7 Variational inference

13.8 Expectation propagation

13.9 Other approximations

14.6 Regularization for regression models

C.1 Getting started with R and Stan

C.2 Fitting a hierarchical model in Stan

C.4 Programming Hamiltonian Monte Carlo in R

And the new chapters:

20 Basis function models

21 Gaussian process models

22 Finite mixture models

23 Dirichlet process models

And there are various little changes throughout.

And, as a reward for those of you who have been patient enough to read this far, here’s a recent paper (by Tomoki Tokuda, Ben Goodrich, Iven Van Mechelen, Francis Tuerlinckx, and myself) on visualizing distributions of covariance matrices:

We present some methods for graphing distributions of covariance matrices and demonstrate them on several models, including the Wishart, inverse-Wishart, and scaled inverse-Wishart families in different dimensions. Our visualizations follow the principle of decomposing a covariance matrix into scale parameters and correlations, pulling out marginal summaries where possible and using two and three-dimensional plots to reveal multivariate structure. Visualizing a distribution of covariance matrices is a step beyond visualizing a single covariance matrix or a single multivariate dataset. Our visualization methods are available through the R package VisCov.

Looking forward to the Gaussian and Dirichlet Process chapters! In addition to the C.1 and C.2 on Stan, I’m assuming that Stan us used throughout the book in places where BUGS/JAGS was previously used? (A big Stan Fan here.)

We haven’t implemented the BDA models in Stan yet. On the other hand, we’re almost done with the Gelman and Hill regression models, which can be found on GitHub at: https://github.com/stan-dev/rstanarm

Also, Stan supports all the covariance and correlation distributions discussed in the paper Andrew references in the post (which I’d recommend if you want to understand covariance priors). And soon, we’ll be optimizing all of them so they’ll be faster.

Your estimated delivery date is:

Thursday, November 21, 2013 -

Saturday, November 23, 2013

Well, all good things are worth waiting for.

Yeah, mine says November also. I didn’t even think to check: is there an electronic version?

Andrew — there were a ton of complaints on Amazon about the Kindle edition of BDA 2 being broken. Do you know if the publishers can get this fixed for version 3?

Andrew, thank you very much for this post! Much appreciated.

[…] Gelman, et al’s Bayesian Data Analysis 3rd edition is coming this Fall! The second edition was a classic, and they’ve added several chapters and polished everything […]

I am a grad student, I am not a fan of you. But your book is pretty darn clear.

BDA is a classic. I am really looking forward to the new book, however, I would really love to buy the PDF version instead of the hardcopy–hence I have not ordered yet. Please let us know if there are any developments on that.

Any word on when we can buy BDA3? The publisher’s page says it’ll be released on 1 November…