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.