相关论文: Bayesian correction of $H(z)$ data uncertainties
The Hessian discretisation method (HDM) for fourth order linear elliptic equations provides a unified convergence analysis framework based on three properties namely coercivity, consistency, and limit-conformity. Some examples that fit in…
In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconception in the…
Computer models are used to model complex processes in various disciplines. Often, a key source of uncertainty in the behavior of complex computer models is uncertainty due to unknown model input parameters. Statistical computer model…
The growth of large-scale structure, together with the geometrical information of cosmic expansion history and cosmological distances, can be used to obtain constraints on the spatial curvature of the universe that probes the early universe…
Discrepancy between the measurements of Hubble constant $H_{0}$ from the cosmic microwave background (CMB) and the local distance ladder is the most serious challenge to the standard $\Lambda$CDM model. Recent researches point out that it…
We set up a suitable renormalization programme for the one-loop computation of the decay rate Gamma(H==>hh) in the Minimal Supersymmetric extension of the Standard Model. We then perform an explicit diagrammatic calculation, including the…
The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. Considering the expansion rate data, the Gaussian linear model can be applied for $\Lambda$CDM,…
The Hubble constant $H_0$ tension has emerged as the most serious crisis in modern cosmology, potentially indicating that the $\Lambda$CDM model may not describe our universe accurately. In this paper, we establish a new,…
Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, the loss ignores misspecification, where models are imperfect. Parameter uncertainties from Bayesian…
Estimation of multiple parameters in an unknown Hamiltonian is investigated. We present upper and lower bounds on the time required to complete the estimation within a prescribed tolerance $\delta$. The lower bound is given on the basis of…
Problems with the concordance cosmology $\Lambda$CDM as the cosmological constant problem, coincidence problems and Hubble tension has led to many proposed alternatives, as the $\Lambda(t)$CDM, where the now called $\Lambda$ cosmological…
The vast majority of stochastic simulation models are imperfect in that they fail to exactly emulate real system dynamics. The inexactness of the simulation model, or model discrepancy, can impact the predictive accuracy and usefulness of…
Fueled by the call for formative assessments, diagnostic classification models (DCMs) have recently gained popularity in psychometrics. Despite their potential for providing diagnostic information that aids in classroom instruction and…
The $O(\alpha_{s})$ corrections to the cross section for $b \bar b \to W^{\pm}H^{\mp}$ at the LHC are calculated in the minimal supersymmetric standard model (MSSM) in the $\bar{MS}$ and OS (on-mass-shell) renormalization schemes. The…
Using a nonparametric function estimation methodology, we present a comparative analysis of the WMAP 1-, 3-, 5-, and 7-year data releases for the CMB angular power spectrum with respect to the following key questions: (a) How well is the…
We constrain two vacuum decay models ($\Lambda(t)$CDM, proposed by the authors of~\cite{Brito:2024bhh}) utilizing the baryon acoustic oscillations (BAO) data released by the Dark Energy Spectroscopic Instrument (DESI), distance prior from…
The high energy physics unfolding problem is an important statistical inverse problem in data analysis at the Large Hadron Collider (LHC) at CERN. The goal of unfolding is to make nonparametric inferences about a particle spectrum from…
Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data could help…
In Bayesian inference, an unknown measurement uncertainty is often quantified in terms of a Gamma distributed precision parameter, which is impractical when prior information on the standard deviation of the measurement uncertainty shall be…
In this paper, we obtain new measurements of the angular homogeneity scale ($\theta_H$) from the BOSS DR12 and eBOSS DR16 catalogs of Luminous Red Galaxies of the Sloan Digital Sky Survey. Considering the flat $\Lambda$CDM model, we use the…