Handbook of Bayesian Variable Selection

TheHandbook of Bayesian Variable Selectionprovides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions.

Features:

* Provides a comprehensive review of methods and applications of Bayesian variable selection.
* Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection.
* Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement.
* Includes contributions by experts in the field.