Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners Article Swipe
Antonio R. Linero
,
Junliang Du
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.21505054
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.21505054
We consider the problem of high-dimensional Bayesian nonparametric variable selection using an aggregation of so-called “weak learners.” The most popular variant of this is the Bayesian additive regression trees (BART) model, which is the natural Bayesian analog to boosting decision trees. In this article, we use Gibbs distributions on random partitions to induce sparsity in ensembles of weak learners. Looking at BART as a special case, we show that the class of Gibbs priors includes two recently proposed models—the Dirichlet additive regression trees (DART) model and the spike-and-forest model—as extremal cases, and we show that certain Gibbs priors are capable of achieving the benefits of both the DART and spike-and-forest models while avoiding some of their key drawbacks. We then show the promising performance of Gibbs priors for other classes of weak learners, such as tensor products of spline basis functions. A Pólya Urn scheme is developed for efficient computations. Supplementary materials for this article are available online.
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https://openalex.org/W4394184933Canonical identifier for this work in OpenAlex
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https://doi.org/10.6084/m9.figshare.21505054Digital Object Identifier
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Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak LearnersWork title
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datasetOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-01-01Full publication date if available
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Antonio R. Linero, Junliang DuList of authors in order
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https://doi.org/10.6084/m9.figshare.21505054Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.6084/m9.figshare.21505054Direct OA link when available
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Prior probability, Nonparametric statistics, Gibbs sampling, Bayesian probability, Econometrics, Statistics, Mathematics, Selection (genetic algorithm), Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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