Articles | Volume 21, issue 6
Nonlin. Processes Geophys., 21, 1145–1157, 2014
https://doi.org/10.5194/npg-21-1145-2014

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

Nonlin. Processes Geophys., 21, 1145–1157, 2014
https://doi.org/10.5194/npg-21-1145-2014

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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Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques
A. R. Ganguly, E. A. Kodra, A. Agrawal, A. Banerjee, S. Boriah, Sn. Chatterjee, So. Chatterjee, A. Choudhary, D. Das, J. Faghmous, P. Ganguli, S. Ghosh, K. Hayhoe, C. Hays, W. Hendrix, Q. Fu, J. Kawale, D. Kumar, V. Kumar, W. Liao, S. Liess, R. Mawalagedara, V. Mithal, R. Oglesby, K. Salvi, P. K. Snyder, K. Steinhaeuser, D. Wang, and D. Wuebbles
Nonlin. Processes Geophys., 21, 777–795, https://doi.org/10.5194/npg-21-777-2014,https://doi.org/10.5194/npg-21-777-2014, 2014

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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: http://pubs.giss.nasa.gov/docs/2008/2008_Bader_etal_1.pdf (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.