Handbook of Bayesian Variable Selection
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- Hardcover ausgewählt
- Taschenbuch
- eBook
-
Sprache:Englisch
204,99 €
UVP
230,60 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Gebundene Ausgabe
Erscheinungsdatum
21.12.2021
Abbildungen
91 SW-Abb., 91 SW-Zeichn., 21 Tabellen
Herausgeber
Mahlet G. Tadesse + weitereVerlag
Taylor & FrancisSeitenzahl
492
Maße (L/B/H)
26/18,3/3,1 cm
Gewicht
1112 g
Sprache
Englisch
ISBN
978-0-367-54376-1
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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