caa a22 4500 605478481 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s10994-014-5475-7 doi (NATIONALLICENCE)springer-10.1007/s10994-014-5475-7 Expectation propagation in linear regression models with spike-and-slab priors [Elektronische Daten] [José Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez] An expectation propagation (EP) algorithm is proposed for approximate inference in linear regression models with spike-and-slab priors. This EP method is applied to regression tasks in which the number of training instances is small and the number of dimensions of the feature space is large. The problems analyzed include the reconstruction of genetic networks, the recovery of sparse signals, the prediction of user sentiment from customer-written reviews and the analysis of biscuit dough constituents from NIR spectra. The proposed EP method outperforms in most of these tasks another EP method that ignores correlations in the posterior and a variational Bayes technique for approximate inference. Additionally, the solutions generated by EP are very close to those given by Gibbs sampling, which can be taken as the gold standard but can be much more computationally expensive. In the tasks analyzed, spike-and-slab priors generally outperform other sparsifying priors, such as Laplace, Student's $$t$$ t and horseshoe priors. The key to the improved predictions with respect to Laplace and Student's $$t$$ t priors is the superior selective shrinkage capacity of the spike-and-slab prior distribution. The Author(s), 2014 Spike-and-slab nationallicence Linear regression nationallicence Expectation propagation nationallicence Selective shrinkage nationallicence Hernández-Lobato José Department of Engineering, University of Cambridge, Trumpington st., CB2 1PZ, Cambridge, UK aut Hernández-Lobato Daniel Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain aut Suárez Alberto Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain aut Machine Learning Springer US; http://www.springer-ny.com 99/3(2015-06-01), 437-487 0885-6125 99:3<437 2015 99 10994 https://doi.org/10.1007/s10994-014-5475-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5475-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Hernández-Lobato José Department of Engineering, University of Cambridge, Trumpington st., CB2 1PZ, Cambridge, UK aut NATIONALLICENCE 700 1- Hernández-Lobato Daniel Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain aut NATIONALLICENCE 700 1- Suárez Alberto Computer Science Department, Universidad Autónoma de Madrid, Francisco Tomás y Valiente, 11, 28049, Madrid, Spain aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 99/3(2015-06-01), 437-487 0885-6125 99:3<437 2015 99 10994