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We use Bayesian model selection paradigms, such as group least absoluteshrinkage and selection operator priors, to facilitate generalized additivemodel selection. Our approach allows for the effects of continuous predictorsto be categorized as either zero, linear or non-linear. Employment of carefullytailored auxiliary variables results in Gibbsian Markov chain Monte Carloschemes for practical implementation of the approach. In addition, mean fieldvariational algorithms with closed form updates are obtained. Whilst not asaccurate, this fast variational option enhances scalability to very large datasets. A package in the R language aids use in practice.

Bayesian Generalized Additive Model Selection Including a Fast Variational Option