Comparative Analysis of EMCEE, Gaussian Process, and Masked Autoregressive Flow in Constraining the Hubble Constant Using Cosmic Chronometers Dataset

[2502.11625] Comparative Analysis of EMCEE, Gaussian Process, and Masked Autoregressive Flow in Constraining the Hubble Constant Using Cosmic Chronometers Dataset Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Astrophysics > Cosmology and Nongalactic Astrophysics arXiv:2502.11625 (astro-ph) [Submitted on 17 Feb 2025 ( v1 ), last revised 9 Apr 2026 (this version, v2)] Title: Comparative Analysis of EMCEE, Gaussian Process, and Masked Autoregressive Flow in Constraining the Hubble Constant Using Cosmic Chronometers Dataset Authors: Jing Niu , Jie-Feng Chen , Peng He , Tong-Jie Zhang , Jie Zhang View a PDF of the paper titled Comparative Analysis of EMCEE, Gaussian Process, and Masked Autoregressive Flow in Constraining the Hubble Constant Using Cosmic Chronometers Dataset, by Jing Niu and 4 other authors View PDF HTML (experimental) Abstract: The Hubble constant ($H_0$) is essential for understanding the universe's evolution. Different methods, such as Affine Invariant Markov chain Monte Carlo Ensemble sampler (EMCEE), Gaussian Process (GP), and Masked Autoregressive Flow (MAF), are used to constrain $H_0$ using $H(z)$ data. However, these methods produce varying $H_0$ values when applied to the same dataset. To investigate these differences, we compare the methods based on their sensitivity to individual data points and their performance in constraining $H_0$. We apply Monte Carlo delete-$d$ jackknife (MCDJ) to assess their sensitivity to individual data points. Our findings reveal that GP is more sensitive to individual data points than both MAF and EMCEE, with MAF being more sensitive than EMCEE. Sensitivity also depends on redshift: EMCEE and GP are more sensitive to $H(z)$ at higher redshifts, while MAF is more sensitive at lower redshifts. In simulation-based performance tests, we generate an ensemble of mock CC datasets with a fixed input truth $H_{0,\mathrm{true}}$, apply each method to recover $H_0$ posteriors, and summarise performance by comparing the recovered posterior to $H_{0,\mathrm{true}}$: (i) posterior central value accuracy (bias and RMSE), (ii) credible-interval calibration (68\% and 95\% coverage), and (iii) overall posterior quality (log score), under two simulation prescriptions ($\Lambda$CDM-based and GP-based). Overall, EMCEE performs best, GP is intermediate, and MAF performs worst across the performance metrics. Comments: 15 pages, 9 figures, 8 tables. Accepted for publication in MNRAS Subjects: Cosmology and Nongalactic Astrophysics (astro-ph.CO) Cite as: arXiv:2502.11625 [astro-ph.CO] (or arXiv:2502.11625v2 [astro-ph.CO] for this version) https://doi.org/10.48550/arXiv.2502.11625 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Mon Not R Astron Soc (2026) Related DOI : https://doi.org/10.1093/mnras/stag700 Focus to learn more DOI(s) linking to related resources Submission history From: Jing Niu [ view email ] [v1] Mon, 17 Feb 2025 10:15:22 UTC (1,220 KB) [v2] Thu, 9 Apr 2026 09:28:28 UTC (881 KB) Full-text links: Access Paper: View a PDF of the paper titled Comparative Analysis of EMCEE, Gaussian Process, and Masked Autoregressive Flow in Constraining the Hubble Constant Using Cosmic Chronometers Dataset, by Jing Niu and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: astro-ph.CO < prev | next > new | recent | 2025-02 Change to browse by: astro-ph References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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