Cryptography: Theory and Practice,Second Edition
Cryptography: Theory and Practice,Second Edition
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Sparse graphical models for exploring gene expression data
Journal of Multivariate Analysis
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
Information-theoretic inference of large transcriptional regulatory networks
EURASIP Journal on Bioinformatics and Systems Biology
Assessing the Impact of Measurement Uncertainty on User Models in Spatial Domains
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Empirical Bayes predictive densities for high-dimensional normal models
Journal of Multivariate Analysis
Analyzing the quality of information solicited from targeted strangers on social media
Proceedings of the 2013 conference on Computer supported cooperative work
Identifying significant edges in graphical models of molecular networks
Artificial Intelligence in Medicine
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We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically as well as computationally. Despite its simplicity, we show that it outperforms eight other entropy estimation procedures across a diverse range of sampling scenarios and data-generating models, even in cases of severe undersampling. We illustrate the approach by analyzing E. coli gene expression data and computing an entropy-based gene-association network from gene expression data. A computer program is available that implements the proposed shrinkage estimator.