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TSP
2010
14 years 11 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
ICML
2007
IEEE
16 years 5 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
UAI
2000
15 years 5 months ago
Utilities as Random Variables: Density Estimation and Structure Discovery
Decision theory does not traditionally include uncertainty over utility functions. We argue that the a person's utility value for a given outcome can be treated as we treat o...
Urszula Chajewska, Daphne Koller
ML
2002
ACM
163views Machine Learning» more  ML 2002»
15 years 3 months ago
Structural Modelling with Sparse Kernels
A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
Steve R. Gunn, Jaz S. Kandola
BIOINFORMATICS
2007
107views more  BIOINFORMATICS 2007»
15 years 4 months ago
SwS: a solvation web service for nucleic acids
Summary: SwS, based on a statistical analysis of crystallographic structures deposited in the NDB, is designed to provide an exhaustive overview of the solvation of nucleic acid s...
Pascal Auffinger, Yaser Hashem