相关论文: Bayesian correction of $H(z)$ data uncertainties

Hyperparameter tuning is a challenging problem especially when the system itself involves uncertainty. Due to noisy function evaluations, optimization under uncertainty can be computationally expensive. In this paper, we present a novel…

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a…

The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or…

This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. This framework uses an existing Fast Fourier Transform (FFT) approach to identify…

Although large language models (LLMs) are highly interactive and extendable, current approaches to ensure reliability in deployments remain mostly limited to rejecting outputs with high uncertainty in order to avoid misinformation. This…

There has been a growing interest in deep learning-based prognostic and health management (PHM) for building end-to-end maintenance decision support systems, especially due to the rapid development of autonomous systems. However, the low…

In this paper, the CMB/BAO measurements which cover the 13 redshift data in the regime $0.106 \leq z \leq 2.34$ are given out. The CMB/BAO samples are based on the BAO distance ratios $r_{s}(z_d)/D_{V}(z)$ and the CMB acoustic scales…

Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion. Thus, using conventional data-driven approaches to build a reliable discriminative model, and…

The current Hubble constant tension is usually presented by comparing constraints on $H_0$ only. However, the post-recombination background cosmic evolution is determined by two parameters in the standard $\Lambda$CDM model, the Hubble…

We present cosmological parameters measurements from the full combination of DESI DR1 galaxy clustering data described with large-scale structure effective field theory. By incorporating additional datasets (photometric galaxies and CMB…

When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertainty estimation in the output of deep object detectors (DNNs)…

The $\Lambda$CDM cosmological model provides to first approximation a good description of the universe, but various tensions with data, most notably Hubble tension, persist. In this work we confront $\Lambda$CDM with the Pantheon Type Ia…

Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model…

We use updated gas mass fraction measurements of 44 massive dynamically relaxed galaxy clusters collated in arXiv:2111.09343 to distinguish between the standard $\Lambda$CDM model and $R_h=ct$ universe. For this purpose, we use Bayesian…

We consider the decays $h\to\gamma\gamma,\gamma Z$ in the context of an extension of the standard model with two inert doublets and an additional $S_3$ symmetry. This model has contributions for these processes through new charged…

A covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the…

The Hubble parameter $H_0$, is not a univocally-defined quantity: it relates redshifts to distances in the near Universe, but is also a key parameter of the $\Lambda$CDM standard cosmological model. As such, $H_0$ affects several physical…

We introduce a statistical measure of the effective model complexity, called the Bayesian complexity. We demonstrate that the Bayesian complexity can be used to assess how many effective parameters a set of data can support and that it is a…

We constrain three cosmological models, i.e. the concordance cold dark matter plus a cosmological constant ($\Lambda$CDM) model, Power-law (PL) model, and $R_h=ct$ model using the available local probes, which includes the JLA compilation…

The Cold Dark Matter (CDM) model has an elegant simplicitly which makes it very predictive, but when its parameters are fixed at their `canonical' values its predictions are in conflict with observational data. There is, however, much…