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JMLR
2006
113views more  JMLR 2006»
15 years 6 months ago
Learning the Structure of Linear Latent Variable Models
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause...
Ricardo Silva, Richard Scheines, Clark Glymour, Pe...
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EOR
2007
90views more  EOR 2007»
15 years 6 months ago
Structural models in consumer credit
We propose a structural credit risk model for consumer lending using option theory and the concept of the value of the consumer’s reputation. Using Brazilian empirical data and ...
Fabio Wendling Muniz de Andrade, Lyn C. Thomas
NAR
2006
164views more  NAR 2006»
15 years 6 months ago
FISH - family identification of sequence homologues using structure anchored hidden Markov models
The FISH server is highly accurate in identifying the family membership of domains in a query protein sequence, even in the case of very low sequence identities to known homologue...
Jeanette Tångrot, Lixiao Wang, Bo Kågs...
IJBRA
2007
97views more  IJBRA 2007»
15 years 6 months ago
Structural Risk Minimisation based gene expression profiling analysis
: For microarray based cancer classification, feature selection is a common method for improving classifier generalisation. Most wrapper methods use cross validation methods to eva...
Xue-wen Chen, Byron Gerlach, Dechang Chen, ZhenQiu...
SYNTHESE
2008
84views more  SYNTHESE 2008»
15 years 6 months ago
Model structure adequacy analysis: selecting models on the basis of their ability to answer scientific questions
Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodne...
Mark L. Taper, David F. Staples, Bradley B. Shepar...