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portada Handbook of Bayesian Variable Selection
Type
Physical Book
Publisher
Author
Year
2021
Language
English
Pages
490
Format
Paperback
ISBN13
9780367543785
Edition No.
1

Handbook of Bayesian Variable Selection

Mahlet Tadesse (Editor) Marina Vannucci (Editor) (Author) · Crc Press 2024-01-29, Boca Raton, · Paperback

Handbook of Bayesian Variable Selection - Mahlet Tadesse (Editor) Marina Vannucci (Editor)

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Synopsis "Handbook of Bayesian Variable Selection"

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

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