Gaussian Process model for fitting deterministic simulator output. Establish efficient and reliable likelihood optimization through hybridized DIRECT-BFGS and multi-start BFGS algorithms. Programming Language: Matlab.


  • Returns prediction, Y, and prediction uncertainty estimate, MSE, for any number of points
  • User is free to select training data. Suggest user scales simulator input to [0,1]^d.
  • Efficient and reliable likelihood optimization.
  • 4 Likelihood optimization routines: DIRECT-BFGS, DIRECT-IF, 0.5d multi-start BFGS, and 2d+1 multi-start BFGS
  • Squared exponential correlation matrix, R.
  • Addition of 'nugget' to R for improved stability of R^-1 and |R| computation.
  • Lower bound on nugget to minimize over-smoothing.
  • Iterative regularization method for improved accuracy when using a nugget.

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Intended Audience

Financial and Insurance Industry, Science/Research, Engineering

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