VictoriaLyu/gpstuff: Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation V1

This is the source code for synthetic experiments and case study in the article "Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation". The code is originally branched from the open source MATLAB library GPstuff  by Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari (2013). GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14(Apr):1175-1179. (Available at http://jmlr.csail.mit.edu/papers/v14/vanhatalo13a.html). Implementations of adaptive design with Gaussian Process and its application in Bermudan option is mainly included in folders "adaptive_design" and "bermudan_option_oracle", with other minor changes in the GPstuff source code according to the experiment setup in the article. 

The title and description of this software/code correspond with the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.