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Description

This paper addresses the question of making inferences regarding Boolean functions under conditions of (i) noise, or stochastic variation in observed data, and (ii) sparsity, by which we mean that the number of inputs or predictors far exceeds the arity of the underlying Boolean function. We put forward a simple probability model for such observations, and discuss model selection, parameter estimation and prediction. We present results on synthetic data and on a proteomic dataset from a study in cancer systems biology.

Details

Title
Sparse, noisy Boolean functions
Creator
Mukherjee, S., Author
Speed, T. P., Author
Published
Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, April 2007
Full Collection Name
Statistics Technical Reports
Other Identifiers
731
Type
Text
Format
technical reports
Extent
17 pages
Archive
Mathematics Statistics Library
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