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

Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show…

In this article the package High-dimensional Metrics (\texttt{hdm}) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on…

In accelerating dark energy models, the estimates of H0 from Sunyaev-Zel'dovich effect (SZE) and X-ray surface brightness of galaxy clusters may depend on the matter content (Omega_M), the curvature (Omega_K) and the equation of state…

In this work, two different models, one with cosmological constant $\Lambda$, and baryonic and dark matter (with $\omega_{dm} \neq 0$), and the other with an $X$ dark energy (with $\omega_{de} \neq -1$), and baryonic and dark matter (with…

The tensions between the values of Hubble constant obtained from the early and the late Universe data pose a significant challenge to modern cosmology. Possible modifications of the flat homogeneous isotropic cosmological {\Lambda}CDM model…

In this paper we compare data to theory. We use a compilation of the most recent cosmic microwave background (CMB) measurements to constrain Hubble's constant h, the baryon fraction Omega_b, and the cosmological constant lambda. We fit h-,…

Quantifying uncertainty in neural network predictions is essential for high-stakes domains such as autonomous driving, healthcare, and manufacturing. While existing approaches often depend on costly sampling or restrictive distributional…

We compile an updated list of 38 measurements of the Hubble parameter $H(z)$ between redshifts $0.07 \leq z \leq 2.36$ and use them to place constraints on model parameters of constant and time-varying dark energy cosmological models, both…

In this work, we present comprehensive observational constraints on the time-varying vacuum $\Lambda(t)$CDM cosmology using the latest baryon acoustic oscillation (BAO) data from Dark Energy Spectroscopic Instrument (DESI) Data Release 2…

Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models. In standard BMC, we construct a discrete probability distribution over the set of possible models, conditional on the observed…

The $\Lambda$CDM cosmolgical model has long been regarded as highly successful in accurately describing a wide range of astronomical observations. However, numerous observational findings have also provided hints of discrepancies from the…

The most recent Hubble constant ($H_0)$ estimates from local methods (z << 1), $H_0=73.8\pm 2.4$ km s$^{-1}$ Mpc$^{-1}$, and the one from high redshifts $H_0=67.3\pm 1.2$ km s$^{-1}$ Mpc$^{-1}$, are discrepant at $2.4 \sigma$ confidence…

The $\Lambda$CDM model provides a good fit to most astronomical observations but harbors large areas of phenomenology and ignorance. With the improvements in the precision and number of observations, discrepancies between key cosmological…

Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the usual slow methods like EM or Gibbs sampling. We provide a…

We analyse various low-redshift cosmological data from Type-Ia Supernova, Baryon Acoustic Oscillations, Time-Delay measurements using Strong-Lensing, $H(z)$ measurements using Cosmic Chronometers and growth measurements from large scale…

Magnitude predictions of $\Lambda$CDM, as parametrized by the Planck collaboration, are not consistent with the supernova data of the whole Pantheon+ sample even when, in order to take into account the uncertainty about its value, the…

Because the baryon-to-photon ratio eta_{10} is in some doubt, we drop nucleosynthetic constraints on eta_{10} and fit the three cosmological parameters (h, Omega_M, eta_{10}) to four observational constraints: Hubble parameter h_o =…

Recent observational results suggest that our universe is nearly flat and well modelled within a $\Lambda$CDM framework. The observed values of $\Omega_{m}$ and $\Omega_{\Lambda}$ inevitably involve uncertainties. Motivated by this, we make…

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional assumptions and thus suffer from the high dimensionality of…

We have assembled a compilation of observational Hubble parameter measurements estimated with the differential evolution of cosmic chronometers, in the redshift range 0<z<1.75. This sample has been used, in combination with CMB data and…