• Corpus ID: 17778480

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

@article{Fisher2014FastBF,
  title={Fast Bayesian Feature Selection for High Dimensional Linear Regression in Genomics via the Ising Approximation},
  author={Charles K. Fisher and Pankaj Mehta},
  journal={ArXiv},
  year={2014},
  volume={abs/1407.8187}
}
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. Here, we introduce a new approach -- the Bayesian Ising Approximation (BIA) -- to rapidly calculate posterior… 

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