1st Edition

Handbook of Bayesian Variable Selection

Edited By Mahlet G. Tadesse, Marina Vannucci Copyright 2022
    490 Pages 91 B/W Illustrations
    by Chapman & Hall

    490 Pages 91 B/W Illustrations
    by Chapman & Hall

    490 Pages 91 B/W Illustrations
    by Chapman & Hall

    Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed.

    The Handbook of Bayesian Variable Selection provides 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.


    • 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.
    • Supported by a website with code, data, and other supplementary material

    1. Discrete Spike-and-Slab Priors: Models and Computational Aspects
    Marina Vannucci

    2. Recent Theoretical Advances with the Discrete Spike-and-Slab Priors
    Shuang Zhou and Debdeep Pati

    3. Theoretical and Computational Aspects of Continuous Spike-and-Slab Priors
    Naveen N. Narisetty

    4. Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO
    Ray Bai, Veronika Ro˘cková, and Edward I. George

    5. Adaptive Computational Methods for Bayesian Variable Selection
    Jim E. Gri□n, Krys G. Latuszynski, and Mark F. J. Steel

    6. Theoretical guarantees for the horseshoe and other global-local shrinkage priors
    Stéphanie van der Pas

    7. MCMC for Global-Local Shrinkage Priors in High-Dimensional Settings
    Anirban Bhattacharya and James Johndrow

    8. Variable Selection with Shrinkage Priors via Sparse Posterior Summaries
    Yan Dora Zhang, Weichang Yu, and Howard D. Bondell

    9. Bayesian Model Averaging in Causal Inference
    Joseph Antonelli and Francesca Dominici

    10. Variable Selection for Hierarchically-Related Outcomes: Models and Algorithms
    H□el□ene Ru□eux, Leonardo Bottolo, and Sylvia Richardson

    11. Bayesian variable selection in spatial regression models
    Brian J. Reich and Ana-Maria Staicu

    12. Effect Selection and Regularization in Structured Additive Distributional Regression
    Paul Wiemann, Thomas Kneib, and Helga Wagner

    13. Sparse Bayesian State-Space and Time-Varying Parameter Models
    Sylvia Fruhwirth-Schnatter and Peter Knaus

    14. Bayesian estimation of single and multiple graphs
    Christine B. Peterson and Francesco C. Stingo

    15. Bayes Factors Based on g-Priors for Variable Selection
    Gonzalo García-Donato and Mark F. J. Steel

    16. Balancing Sparsity and Power: Likelihoods, Priors, and Misspecification
    David Rossell and Francisco Javier Rubio

    17. Variable Selection and Interaction Detection with Bayesian Additive Regression Trees
    Carlos M. Carvalho, Edward I. George, P. Richard Hahn, and Robert E. McCulloch

    18. Variable Selection for Bayesian Decision Tree Ensembles
    Antonio R. Linero and Junliang Du

    19. Stochastic Partitioning for Variable Selection in Multivariate Mixture of Regression Models
    Stefano Monni and Mahlet G. Tadesse


    Mahlet Tadesse is Professor and Chair in the Department of Mathematics and Statistics at Georgetown University, USA. Her research over the past two decades has focused on Bayesian modeling for high-dimensional data with an emphasis on variable selection methods and mixture models. She also works on various interdisciplinary projects in genomics and public health. She is a recipient of the Myrto Lefkopoulou Distinguished Lectureship award, an elected member of the International Statistical Institute and an elected fellow of the American Statistical Association.

    Marina Vannucci is Noah Harding Professor of Statistics at Rice University, USA. Her research over the past 25 years has focused on the development of methodologies for Bayesian variable selection in linear settings, mixture models and graphical models, and on related computational algorithms. She also has a solid history of scientific collaborations and is particularly interested in applications of Bayesian inference to genomics and neuroscience. She has received an NSF CAREER award and the Mitchell prize by ISBA for her research, and the Zellner Medal by ISBA for exceptional service over an extended period of time with long-lasting impact. She is an elected Member of ISI and RSS and an elected fellow of ASA, IMS, AAAS and ISBA.

    "This book provides a comprehensive review of Bayesian variable selection methods written by various distinguished experts and covers recent theoretical, methodological, and computational advancements in the field. In my opinion, it is an excellent reference book for researchers at all levels."
    ~Yang Ni, Texas A&M University