We introduce statistical techniques required to handle complex computer models with potential applications to astronomy. Computer experiments play a critical role in almost all fields of scientific research and engineering. These computer experiments, or simulators, are often computationally expensive, leading to the use of emulators for rapidly approximating the outcome of the experiment. Gaussian process models, also known as Kriging, are the most common choice of emulator. While emulators offer significant improvements in computation over computer simulators, they require a selection of inputs along with the corresponding outputs of the computer experiment to function well. Thus, it is important to select inputs judiciously for the full computer simulation to construct an accurate emulator. Space-filling designs are efficient when the general response surface of the outcome is unknown, and thus they are a popular choice when selecting simulator inputs for building an emulator. In this tutorial we discuss how to construct these space filling designs, perform the subsequent fitting of the Gaussian process surrogates, and briefly indicate their potential applications to astronomy research.

Gaussian Processes Modeling [R package GPfit version 1.0-8]

Citations

Statistical Analysis of Complex Computer Models in Astronomy

Related Papers (4)

Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering [R package ClusVis version 1.2.0]

EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution [R package EMCluster version 0.2-12]

Locally Gaussian Distributions: Estimation and Methods [R package lg version 0.4.1]

Fast methods for training Gaussian processes