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

The 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 ty...

Full description

Saved in:
Bibliographic Details
Published in:The Astrophysical journal. Supplement series 2023-06, Vol.266 (2), p.27
Main Authors: Zhang, Hao, Wang, Yu-Chen, Zhang, Tong-Jie, Zhang, Tingting
Format: Article
Language:English
Subjects:
Astronomy data analysis
Astrostatistics strategies
Bayes factor
Bayesian analysis
Computation
Cosmology
Gamma ray bursts
Gamma rays
Gaussian process
Gaussian Processes regression
Kernel functions
Measuring instruments
Observational cosmology
Radial basis function
Rejection
Sampling
Supernovae
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The 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, type Ia supernovae data, and gamma-ray burst data, using five kernel functions. To evaluate the differences between kernel functions, we assess the strength of evidence using Bayes factors. Our results show that, for ABC rejection, the Matérn kernel with ν = 5/2 (M52 kernel) outperformes the commonly used radial basis function (RBF) kernel in approximating all three data sets. Bayes factors indicate that the M52 kernel typically supports the observed data better than the RBF kernel but with no clear advantage over other alternatives. However, nested sampling gives different results, with the M52 kernel losing its advantage. Nevertheless, Bayes factors indicate no significant dependence of the data on each kernel.
ISSN:0067-0049
1538-4365
DOI:10.3847/1538-4365/accb92