Kernel-Based Spatial Covariance is a spatial covariance modeling method that employs kernel functions to quantify the similarity between spatial locations as a function of their relative positions, thereby enabling flexible, non-parametric inference of spatial dependence structures.
5
Publications
195
Citations
10
Authors
4
Institutions
Publications per year
2017–2026
1
Parents
Publications
5
CONSTRUCTING A FLEXIBLE LIKELIHOOD FUNCTION FOR SPECTROSCOPIC INFERENCE
Ian Czekala, Sean M. Andrews, Kaisey S. Mandel et al. · The Astrophysical Journal · 2015 · 134 citations · Full text
Natural Sciences, Astronomy, Physics +17
Kernel covariance image region description for object tracking
Omar Arif, Patricio A. Vela · 2009 · 14 citations
1–5 of 5
Authors
10
- KSKaisey S. Mandel
Center for Astrophysics Harvard & Smithsonian, United States
1 publications · 134 citations
1
h-index
- XS
1
h-index
- ICIan Czekala
Center for Astrophysics Harvard & Smithsonian, United States
1 publications · 134 citations
1
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- DW
1
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- HA
1
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1–5 of 10
Institutions
4
Center for Astrophysics Harvard & Smithsonian
Cambridge, United States
1 h-index
4 publications
536 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
2 publications
28 citations
New York University
New York, United States
1 h-index
1 publications
134 citations
Widener University
Chester, United States
1 h-index
2 publications
40 citations
University of Glasgow
Glasgow, United Kingdom
1 publications
11 citations
1–5 of 4
Venues
- Oikos
1 publications · 16 citations
1
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- The Astrophysical Journal
ApJ · 1 publications · 134 citations
1
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- Ecology Letters
1 publications · 11 citations
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