Exploratory-Phase-Free Estimation of GP Hyperparameters in Sequential Design Methods-At the Example of Bayesian Inverse Problems.

ABSTRACT: Methods for sequential design of computer experiments typically consist of two phases. In the first phase, the exploratory phase, a space-filling initial design is used to estimate hyperparameters of a Gaussian process emulator (GPE) and to provide some initial global exploration of the model function. In the second phase, more design points are added one by one to improve the GPE and to solve the actual problem at hand (e.g., Bayesian optimization, estimation of failure probabilities, solving Bayesian inverse problems). In this article, we investigate whether hyperparameters can be estimated without a separate exploratory phase. Such an approach will leave hyperparameters uncertain in the first iterations, so the acquisition function (which tells where to evaluate the model function next)

SUBMITTER: Sinsbeck M 

PROVIDER: S-EPMC7861299 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature