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Related papers: Bayesian correction of $H(z)$ data uncertainties

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We compute the Bayesian Evidence for models considered in the main analysis of Planck cosmic microwave background data. By utilising carefully-defined nearest-neighbour distances in parameter space, we reuse the Monte Carlo Markov Chains…

Cosmology and Nongalactic Astrophysics · Physics 2017-09-13 Alan Heavens , Yabebal Fantaye , Elena Sellentin , Hans Eggers , Zafiirah Hosenie , Steve Kroon , Arrykrishna Mootoovaloo

It is shown that a subelectronvolt upper limit can be derived on the neutrino mass from the CMB data alone in the Lambda CDM model with the power-law adiabatic perturbations, without the aid of any other cosmological data. Assuming the…

Astrophysics · Physics 2016-08-30 Kazuhide Ichikawa , Masataka Fukugita , Masahiro Kawasaki

Hyper-differential sensitivity analysis with respect to model discrepancy was recently developed to enable uncertainty quantification for optimization problems. The approach consists of two primary steps: (i) Bayesian calibration of the…

Numerical Analysis · Mathematics 2025-10-09 Joseph Hart , Bart van Bloemen Waanders , Jixian Li , Timbwaoga A. J. Ouermi , Chris R. Johnson

Large language models (LLMs) have been proposed as alternatives to human experts for estimating unknown quantities with associated uncertainty, a process known as Bayesian elicitation. We test this by asking eleven LLMs to estimate…

Artificial Intelligence · Computer Science 2026-04-03 Luka Hobor , Mario Brcic , Mihael Kovac , Kristijan Poje

Despite the remarkable success of the $\Lambda$Cold Dark Matter ($\Lambda$CDM) cosmological model, a growing discrepancy has emerged (currently measured at the level of $\sim 4-6 \sigma$) between the value of the Hubble constant $H_0$…

Cosmology and Nongalactic Astrophysics · Physics 2022-09-07 Nils Schöneberg , Guillermo Franco Abellán , Andrea Pérez Sánchez , Samuel J. Witte , Vivian Poulin , Julien Lesgourgues

Two of the most significant challenges in uncertainty quantification pertain to the high computational cost for simulating complex physical models and the high dimension of the random inputs. In applications of practical interest, both of…

Computational Engineering, Finance, and Science · Computer Science 2022-09-02 Jonas Nitzler , Jonas Biehler , Niklas Fehn , Phaedon-Stelios Koutsourelakis , Wolfgang A. Wall

Unmeasured confounding bias threatens the validity of observational studies. While sensitivity analyses and study designs have been proposed to address this issue, they often overlook the growing availability of auxiliary data. Using…

Methodology · Statistics 2024-10-22 Jie Kate Hu , Dafne Zorzetto , Francesca Dominici

Some of the arguments which support the strong concensus for an $\Omega_o$ = 0.3, $\lambda_o$ = 0.7 model are reexamined. Corrections for Malmquist bias, local flow and metallicity suggest a revised value for $H_o$ of 63 $\pm$ 6 km/s/Mpc,…

Astrophysics · Physics 2016-11-03 M. Rowan-Robinson

Since the baryon-to-photon ratio "eta" is in some doubt at present, we ignore the constraints on eta from big bang nucleosynthesis (BBN) and fit the three key cosmological parameters (h, Omega_M, eta) to four other observational…

Astrophysics · Physics 2022-03-23 G. Steigman , N. Hata , J. E. Felten

We revisit the kink-like parametrization of the deceleration parameter q(z), which considers a transition, at $z_t$, from cosmic deceleration to acceleration. In this parametrization the initial, at $z \gg z_t$, value of the q-parameter is…

Cosmology and Nongalactic Astrophysics · Physics 2016-03-01 Marcelo Vargas dos Santos , Ribamar R. R. Reis , Ioav Waga

We present a determination of the Hubble constant from measurements of the Sunyaev-Zeldovich Effect (SZE) in an orientation-unbiased sample of 7 z < 0.1 galaxy clusters. With improved X-ray models and a more accurate 32-GHz calibration, we…

Astrophysics · Physics 2017-08-23 Brian S. Mason , Steven T. Myers , A. C. S. Readhead

Estimating predictive uncertainty is crucial for many computer vision tasks, from image classification to autonomous driving systems. Hamiltonian Monte Carlo (HMC) is an sampling method for performing Bayesian inference. On the other hand,…

Machine Learning · Computer Science 2019-07-03 Diego Vergara , Sergio Hernández , Matias Valdenegro-Toro , Felipe Jorquera

Hierarchical Bayesian models are increasingly used in large, inhomogeneous complex network dynamical systems by modeling parameters as draws from a hyperparameter-governed distribution. However, theoretical guarantees for these estimates as…

Statistics Theory · Mathematics 2026-01-23 Yi Yu , Yubo Hou , Yinchong Wang , Nan Zhang , Jianfeng Feng , Wenlian Lu

We make two contributions to the problem of estimating the $L_1$ calibration error of a binary classifier from a finite dataset. First, we provide an upper bound for any classifier where the calibration function has bounded variation.…

$\Lambda$CDM provides a leading framework in the interpretation of modern cosmology. Nevertheless, the scientific community still struggles with many open problems in cosmology. Among the most noticeable ones, the tension in the Hubble…

Cosmology and Nongalactic Astrophysics · Physics 2024-12-09 B. De Simone , M. H. P. M. van Putten , M. G. Dainotti , G. Lambiase

We constrain flat cosmological models with a joint likelihood analysis of a new compilation of data from the cosmic microwave background (CMB) and from the 2dF Galaxy Redshift Survey (2dFGRS). Fitting the CMB alone yields a known degeneracy…

A complete calculation of the ${\cal O}(\alpha_s^4)$ perturbative QCD corrections to the hadronic decay width of the $Z$-boson has recently been performed by Baikov et al.[1]. In their analysis, Baikov et al. relied on the conventional…

High Energy Physics - Phenomenology · Physics 2014-08-29 Sheng-Quan Wang , Xing-Gang Wu , Stanley J. Brodsky

Cosmic Microwave Background (CMB) independent approaches are frequently used in the literature to provide estimates of Hubble constant ($H_0$). In this work, we report CMB independent constraints on $H_0$ in an anisotropic extension of…

Cosmology and Nongalactic Astrophysics · Physics 2023-11-13 Vikrant Yadav

Uncertainty quantification (UQ) methods for Large Language Models (LLMs) encompass a variety of approaches, with two major types being particularly prominent: information-based, which focus on model confidence expressed as token…

Computation and Language · Computer Science 2025-12-10 Roman Vashurin , Maiya Goloburda , Albina Ilina , Aleksandr Rubashevskii , Preslav Nakov , Artem Shelmanov , Maxim Panov
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