Handbook of Bayesian Variable Selection

A comprehensive review of Bayesian variable selection methods and applications, with R code provided in the online supplement.

Format: Hardback
Length: 490 pages
Publication date: 20 December 2021
Publisher: Taylor & Francis Ltd

Bayesian variable selection is a powerful tool in statistical inference that allows researchers to efficiently and effectively analyze complex data sets. This comprehensive review delves into the various methods and applications of Bayesian variable selection, encompassing a wide range of topics.

The book is organized into four distinct parts, each dedicated to exploring different aspects of Bayesian variable selection. The first part, "Spike-and-Slab Priors," provides an in-depth exploration of the use of spike-and-slab priors in Bayesian variable selection. Spike-and-slab priors are a type of prior distribution that assigns a probability distribution to a continuous variable by dividing it into a finite number of intervals or "slabs." This approach allows for a more flexible and interpretable model, as it captures the inherent uncertainty in the data.

The second part, "Continuous Shrinkage Priors," focuses on the application of continuous shrinkage priors in Bayesian variable selection. Continuous shrinkage priors are a family of priors that impose a smooth and flexible distribution on the parameters of a model. They are particularly useful when dealing with high-dimensional data or data with a large number of variables. By shrinking the parameter estimates towards a common mean, continuous shrinkage priors can effectively reduce the variance and improve the accuracy of the model.

The third part, "Extensions to Various Modeling," explores the use of Bayesian variable selection in various modeling scenarios. This includes the application of Bayesian variable selection to linear and nonlinear regression models, survival analysis, and time-series analysis. The book provides worked-out examples with R code provided in the online supplement, allowing readers to apply the techniques discussed in the text to their own data sets.

The fourth part, "Other Approaches to Bayesian Variable Selection," discusses alternative approaches to Bayesian variable selection, such as Markov chain Monte Carlo (MCMC) methods and Bayesian network inference. MCMC methods are a powerful tool for Bayesian inference, as they allow for the sampling of the posterior distribution of the parameters. Bayesian network inference, on the other hand, is a graphical model that represents the relationships between variables in a system. By combining these approaches, researchers can gain a deeper understanding of the underlying relationships in the data and make more informed decisions.

Contributions by experts in the field make this book an invaluable resource for researchers and practitioners in the fields of statistics, data analysis, and machine learning. The book covers theoretical and methodological aspects of Bayesian variable selection, providing a solid foundation for understanding the underlying principles and applications of the technique.

In conclusion, Bayesian variable selection is a powerful tool that enables researchers to analyze complex data sets more efficiently and effectively. This comprehensive review provides a thorough exploration of the methods and applications of Bayesian variable selection, covering a wide range of topics and providing worked-out examples with R code. With contributions by experts in the field, this book is an invaluable resource for anyone interested in developing their understanding of Bayesian variable selection and applying it to their own research.

Weight: 1066g
Dimension: 184 x 260 x 37 (mm)
ISBN-13: 9780367543761