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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 6
Nonlin. Processes Geophys., 21, 1145–1157, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Physics-driven data mining in climate change and weather...

Nonlin. Processes Geophys., 21, 1145–1157, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Dec 2014

Research article | 01 Dec 2014

Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling

D. Das et al.

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Cited articles

Antoniak, C.: Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems, Ann. Stat., 2, 1152–1174, 1974.
Bader, D. C., Covey, C., Gutkowski Jr., W. J., Held, I. M., Kunkel, K. E., Miller, R. L., Tokmakian, R. T., and Zhang, M. H.: Climate Models: An Assessment of Strengths and Limitations, US Climate Change Science Program Synthesis and Assessment Product 3.1, Department of Energy, Office of Biological and Environmental Research, 124 pp., available at: (last access: 20 July 2014), 2008.
Basu, S., Bilenko, M., Banerjee, A., and Mooney, R.: Probabilistic semi-supervised clustering with constraints, J. Mach. Learn. Res., 71–98, 2006.
Benestad, R., Hanssen-Bauer, I., and Chen, D.: Empirical-Statistical Downscaling, World Scientific Publishing Company, New Jersey, London, 2008.
Bishop, C. and Svenskn, M.: Bayesian hierarchical mixtures of experts, in: Uncertainty in Artificial Intelligence, Morgan Kaufman, San Francisco, CA, 57–64, 2002.
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