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UAI
1996
13 years 9 months ago
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomp...
David Maxwell Chickering, David Heckerman
KDD
2012
ACM
238views Data Mining» more  KDD 2012»
11 years 10 months ago
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data
Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiativ...
Lei Yuan, Yalin Wang, Paul M. Thompson, Vaibhav A....
CIMAGING
2008
142views Hardware» more  CIMAGING 2008»
13 years 9 months ago
Greedy signal recovery and uncertainty principles
This paper seeks to bridge the two major algorithmic approaches to sparse signal recovery from an incomplete set of linear measurements
Deanna Needell, Roman Vershynin
BMCBI
2011
13 years 2 months ago
Meta-analysis of gene expression microarrays with missing replicates
Background: Many different microarray experiments are publicly available today. It is natural to ask whether different experiments for the same phenotypic conditions can be combin...
Fan Shi, Gad Abraham, Christopher Leckie, Izhak Ha...
TSMC
2010
13 years 2 months ago
Incomplete Multigranulation Rough Set
The original rough-set model is primarily concerned with the approximations of sets described by a single equivalence relation on a given universe. With granular computing point of...
Yuhua Qian, Jiye Liang, Chuangyin Dang