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» On Bayesian model and variable selection using MCMC
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BIOINFORMATICS
2006
92views more  BIOINFORMATICS 2006»
14 years 9 months ago
What should be expected from feature selection in small-sample settings
Motivation: High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis; howev...
Chao Sima, Edward R. Dougherty
BMCBI
2010
185views more  BMCBI 2010»
14 years 4 months ago
MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heu
Background: The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most compara...
Raphaël Helaers, Michel C. Milinkovitch
BMCBI
2006
153views more  BMCBI 2006»
14 years 9 months ago
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating v...
Maureen A. Sartor, Craig R. Tomlinson, Scott C. We...
WSC
2004
14 years 11 months ago
A Large Deviations Perspective on Ordinal Optimization
We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studi...
Peter W. Glynn, Sandeep Juneja
KDD
2009
ACM
191views Data Mining» more  KDD 2009»
15 years 10 months ago
Scalable pseudo-likelihood estimation in hybrid random fields
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
Antonino Freno, Edmondo Trentin, Marco Gori