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.

Related authors

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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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Revised manuscript accepted for NPG
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Cited articles

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