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FLAIRS
2006
15 years 5 months ago
Decomposing Local Probability Distributions in Bayesian Networks for Improved Inference and Parameter Learning
A major difficulty in building Bayesian network models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with th...
Adam Zagorecki, Mark Voortman, Marek J. Druzdzel
CSDA
2007
87views more  CSDA 2007»
15 years 4 months ago
Estimation and inference in functional mixed-effects models
Functional mixed-effects models are very useful in analyzing functional data. A general functional mixed-effects model that inherits the flexibility of linear mixed-effects model...
Anestis Antoniadis, Theofanis Sapatinas
JMLR
2008
209views more  JMLR 2008»
15 years 4 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
EOR
2007
85views more  EOR 2007»
15 years 4 months ago
Reject inference, augmentation, and sample selection
Many researchers see the need for reject inference in credit scoring models to come from a sample selection problem whereby a missing variable results in omitted variable bias. Al...
John Banasik, Jonathan Crook
ARTMED
2005
87views more  ARTMED 2005»
15 years 4 months ago
Fuzzy attributes of a DNA complex: Development of a fuzzy inference engine for codon-"junk" codon delineation
Codons refer to those parts in a DNA complex encoded towards forming a desired set of proteins. Also coexist in the DNA are noncodons (or "junk" codons) whose functions ...
Tomás Arredondo Vidal, Perambur S. Neelakan...