1. Allaire, D., Willcox, K., and Toupet, O. (2010). A Bayesian-based approach to multifidelity multidisciplinary design optimization. In 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, page 9183.
2. Alvarez, M. A. and Lawrence, N. D. (2011). Computationally efficient convolved multiple output gaussian processes. Journal of Machine Learning Research, 12(May):1459–1500.
3. Alvarez, M. A., Rosasco, L., Lawrence, N. D., et al. (2012). Kernels for vector-valued functions: A review. Foundations and Trends® in Machine Learning, 4(3):195–266.
4. Boyle, P. and Frean, M. (2005). Dependent gaussian processes. In Advances in neural information processing systems, pages 217–224.
5. Christensen, D. E. (2012). Multifidelity methods for multidisciplinary design under uncertainty. PhD thesis, Massachusetts Institute of Technology.