Kernel Selection for Gaussian Process in Cosmology: with Approximate Bayesian Computation Rejection and Nested Sampling

Understand

Gaussian Process (GP) has gained much attention in cosmology due to its ability to reconstruct cosmological data in a model-independent manner.

• In this study, we compare two methods for GP kernel selection: Approximate Bayesian Computation (ABC) Rejection and nested sampling.
• We analyze three types of data: cosmic Chronometer data (CC), Type Ia Supernovae (SNIa), and Gamma Ray Burst (GRB), using five kernel functions.
• To evaluate the differences between kernel functions, we assess the strength of evidence using Bayes factors.