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Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution.

T. Paananen, J. Piironen, M. Andersen, and A. Vehtari. AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1743-1752. PMLR, (2019)

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On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior.J. Piironen, and A. Vehtari. AISTATS, volume 54 of Proceedings of Machine Learning Research, page 905-913. PMLR, (2017)Projective Inference in High-dimensional Problems: Prediction and Feature Selection.J. Piironen, M. Paasiniemi, and A. Vehtari. CoRR, (2018)Alarm Prediction in LTE Networks.S. Holmbacka, J. Niemelä, H. Karikallio, K. Sunila, I. Raiskinen, E. Siivola, J. Piironen, and T. Sivula. ICT, page 341-345. IEEE, (2018)Bayesian Predictive Inference and Feature Selection for High-Dimensional Data; (Bayesilainen ennustava päättely ja piirrevalinta korkeaulotteisille aineistoille).J. Piironen. Aalto University, Espoo, Finland, (2019)base-search.net (ftaaltouniv:oai:aaltodoc.aalto.fi:123456789/37837).Iterative Supervised Principal Components.J. Piironen, and A. Vehtari. AISTATS, volume 84 of Proceedings of Machine Learning Research, page 106-114. PMLR, (2018)Comparison of Bayesian predictive methods for model selection.J. Piironen, and A. Vehtari. Statistics and Computing, 27 (3): 711-735 (2017)Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution.T. Paananen, J. Piironen, M. Andersen, and A. Vehtari. AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1743-1752. PMLR, (2019)Projection predictive model selection for Gaussian processes.J. Piironen, and A. Vehtari. MLSP, page 1-6. IEEE, (2016)

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Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution.

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Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution.