Approximation Methods for Gaussian Process Regression

  • Joaquin Quiñonero Candela ,
  • Carl Edward Ramussen ,
  • Christopher K. I. Williams

MSR-TR-2007-124 |


A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.