Related papers: The Value of $H_0$ from Gaussian Processes
We present a new Hubble parameterization method and employ observational data from Hubble, Pantheon, and Baryon Acoustic Oscillations to constrain model parameters. The proposed method is thoroughly validated against these datasets,…
Gaussian processes (GPs) are frequently used in machine learning and statistics to construct powerful models. However, when employing GPs in practice, important considerations must be made, regarding the high computational burden,…
We apply a tension metric $Q_\textrm{UDM}$, the update difference in mean parameters, to understand the source of the difference in the measured Hubble constant $H_0$ inferred with cosmic microwave background lensing measurements from the…
Gaussian Process State Space Models (GP-SSMs) are a non-parametric model class suitable to represent nonlinear dynamics. They become increasingly popular in data-driven modeling approaches, i.e. when no first-order physics-based models are…
We present novel Monte Carlo (MC) and multilevel Monte Carlo (MLMC) methods to determine the unbiased covariance of random variables using h-statistics. The advantage of this procedure lies in the unbiased construction of the estimator's…
The Hubble constant ${H}_0$ is a crucial parameter in cosmology. However, different cosmic observations have resulted in varying posterior results for ${H}_0$, leading to what is known as the ${H}_0$ tension. In order to address this issue,…
Gaussian process (GP) regression is a non-parametric, Bayesian framework to approximate complex models. Standard GP regression can lead to an unbounded model in which some points can take infeasible values. We introduce a new GP method that…
Adopting the low-redshift observational datasets, including the Pantheon sample of Type Ia supernovae, baryon acoustic oscillation measurements, and the tomographic Alcock-Paczynski method, we determine the Hubble constant to be…
We present a Hamiltonian Monte Carlo algorithm to sample from multivariate Gaussian distributions in which the target space is constrained by linear and quadratic inequalities or products thereof. The Hamiltonian equations of motion can be…
In this paper we introduce a novel online time series forecasting model we refer to as the pM-GP filter. We show that our model is equivalent to Gaussian process regression, with the advantage that both online forecasting and online…
We introduce a novel Bayesian approach for variable selection using Gaussian process regression, which is crucial for enhancing interpretability and model regularization. Our method employs nearest neighbor Gaussian processes, serving as…
The recent measurements of the cosmological parameter $H_0$ from the direct local observations and the inferred value from the Cosmic Microwave Background show $\sim 4 \sigma$ discrepancy. This may indicate new physics beyond the standard…
In the realm of the $\omega$CDM cosmological model with quiescence or quintessence as the dark energy, characterized by $\omega>-1$, there exists a fixed value of $H(z)$ at $z=-1$, devoid of dependency on other cosmological parameters. To…
Quantum metrology pursues high-precision measurements of physical quantities by using quantum resources. However, the decoherence generally hinders its performance. Previous work found that the metrological error tends to diverge in the…
Spectral approximation and variational inducing learning for the Gaussian process are two popular methods to reduce computational complexity. However, in previous research, those methods always tend to adopt the orthonormal basis functions,…
Taking advantage of Gaussian process (GP), we obtain an improved estimate of the Hubble constant, $H_0=70.41\pm1.58$ km s$^{-1}$ Mpc$^{-1}$, using Hubble parameter [$H(z)$] from cosmic chronometers (CCH) and expansion rate function…
Using a fully Bayesian approach, Gaussian Process regression is extended to include marginalisation over the kernel choice and kernel hyperparameters. In addition, Bayesian model comparison via the evidence enables direct kernel comparison.…
In [26] it had been found that gravitational particle production (to be more specific, gravitational vacuum polarization) results in an effective increase in the directly measured value of the Hubble constant $H_0$ while it does not affect…
Measurements of the Hubble constant, $H_0$, from the cosmic distance ladder are currently in tension with the value inferred from Planck observations of the CMB and other high redshift datasets if a flat $\Lambda$CDM cosmological model is…
We consider a measurable stationary Gaussian stochastic process. A criterion for testing hypotheses about the covariance function of such a process using estimates for its norm in the space $L_p(\mathbb {T}),\,p\geq1$, is constructed.