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» On Bayesian model and variable selection using MCMC
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UAI
2000
14 years 11 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
BMCBI
2010
178views more  BMCBI 2010»
14 years 9 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
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ICDM
2006
IEEE
122views Data Mining» more  ICDM 2006»
15 years 3 months ago
Optimal Segmentation Using Tree Models
Sequence data are abundant in application areas such as computational biology, environmental sciences, and telecommunications. Many real-life sequences have a strong segmental str...
Robert Gwadera, Aristides Gionis, Heikki Mannila
JCB
2006
185views more  JCB 2006»
14 years 9 months ago
Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
Andrew Golightly, Darren J. Wilkinson
JMLR
2010
97views more  JMLR 2010»
14 years 4 months ago
Evaluation of a Bayesian model-based approach in GA studies
In a typical Genetic Association Study (GAS) several hundreds to millions of genomic variables are measured and tested for association with a given set of a phenotypic variables (...
Gábor Hullám, Peter Antal, Csaba Sza...