Fast Bayesian Feature Selection for High Dimensional Linear Regression in Genomics via the Ising Approximation

30 Jul 2014 Charles K. Fisher Pankaj Mehta

Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning. Feature selection is especially difficult and computationally intensive when the number of variables approaches or exceeds the number of samples, as is often the case for many genomic datasets... (read more)

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