gamselBayesVignette

The vignette of the gamselBayes package is displayed using the default PDF file browser. It provides a detailed description of use of the package for Bayesian generalized additive model selection.

Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2023) <arXiv:2201.00412>.

Matt Wand

gamselBayes

Bayesian Generalized Additive Model Selection

Virginia X. He

Matt P. Wand

gamselBayesVignette function

Virginia X. He <a href="/link/virginia.x.he%40student.uts.edu.au?package=gamselBayes&version=2.0-1" data-mini-rdoc="gamselBayes::virginia.x.he@student.uts.edu.au">virginia.x.he@student.uts.edu.au</a> and Matt P. Wand <a href="/link/matt.wand%40uts.edu.au?package=gamselBayes&version=2.0-1" data-mini-rdoc="gamselBayes::matt.wand@uts.edu.au">matt.wand@uts.edu.au</a>

Author

Display the package's vignette. — gamselBayesVignette

Virginia X. He <a href='mailto:virginia.x.he@student.uts.edu.au'>virginia.x.he@student.uts.edu.au</a> and Matt P. Wand <a href='mailto:matt.wand@uts.edu.au'>matt.wand@uts.edu.au</a>

gamselBayesVignette: Display the package's vignette.

Description

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Examples