Butler, A., Haynes, R., Humphries, T.D. and Ranjan, P. (2014) Efficient Optimization of the Likelihood Function in Gaussian Process Modeling. Computational Statistics and Data Analysis, 73, 40-52.
http://dx.doi.org/10.1016/j.csda.2013.11.017

has been cited by the following article:

• TITLE: Inverse Problem for a Time-Series Valued Computer Simulator via Scalarization

  AUTHORS: Pritam Ranjan, Mark Thomas, Holger Teismann, Sujay Mukhoti

  KEYWORDS: Calibration, Computer Experiments, Contour Estimation, Gaussian Process Model, Non-Stationary Process, Sequential Design

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.3, June 28, 2016

  ABSTRACT: For an expensive to evaluate computer simulator, even the estimate of the overall surface can be a challenging problem. In this paper, we focus on the estimation of the inverse solution, i.e., to find the set(s) of input combinations of the simulator that generates a pre-determined simulator output. Ranjan et al. [1] proposed an expected improvement criterion under a sequential design framework for the inverse problem with a scalar valued simulator. In this paper, we focus on the inverse problem for a time-series valued simulator. We have used a few simulated and two real examples for performance comparison.