Linear-Time Inverse Covariance Matrix Estimation in Gaussian Processes

@inproceedings{Gonzalez2008LinearTimeIC,
  title={Linear-Time Inverse Covariance Matrix Estimation in Gaussian Processes},
  author={Joseph Gonzalez},
  year={2008}
}
The computational cost of Gaussian process regression grows cubically with respect to the number of variables due to the inversion of the covariance matrix, which is impractical for data sets with more than a few thousand nodes. Furthermore, Gaussian processes lack the ability to represent conditional independence assertions between variables. We describe iterative proportional scaling for directly estimating the precision matrix without inverting the covariance matrix, given an undirected… CONTINUE READING

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Log-linear and gaussian graphical models, saint flour summerschool lectures

  • S. Lauritzen
  • http://www.stats.ox.ac.uk/ steffen/stflour/sf5c…
  • 2006
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