Running vacuum model versus ΛCDM – a Bayesian analysis

Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model

Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...

Modified Gravity Theories: Distinguishing from ΛCDM Model

The method and probability of distinguishing between the Λ cold dark matter (ΛCDM) model and modified gravity are studied from future observations for the growth rate of cosmic structure (Euclid redshift survey). We compare the mock observational data to the theoretical cosmic growth rate by modified gravity models, including the extended Dvali–Gabadadze–Porrati (DGP) model, kinetic gravity bra...

Voids in a ΛCDM Universe

We study the formation and evolution of voids in the dark matter distribution using various simulations of the popular Λ Cold Dark Matter cosmogony. We identify voids by requiring them to be regions of space with a mean overdensity of −0.8 or less – roughly the equivalent of using a Spherical Overdensity group finder for haloes. Each of the simulations contains thousands of voids. The distribut...

A Bayesian nonparametric meta-analysis model.

In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but f...

Bayesian Analysis of a Stochastic Volatility Model