相关论文: Bayesian analysis of the backreaction models

We review the work done so far aimed at modeling in an alternative way the dark matter in the Universe: the scalar field/ Bose-Einstein condensate dark matter (SFDM/BEC) model. We discuss a number of important achievements and…

We establish concentration rates for estimation of treatment effects in experiments that incorporate prior sources of information -- such as past pilots, related studies, or expert assessments -- whose external validity is uncertain. Each…

Regression models for circular variables are less developed, since the concept of building a linear predictor from linear combinations of covariates and various random effects, breaks the circular nature of the variable. In this paper, we…

In the estimation of the causal effect under linear Structural Causal Models (SCMs), it is common practice to first identify the causal structure, estimate the probability distributions, and then calculate the causal effect. However, if the…

Recent cosmological analyses based on DESI and CMB data have revealed a tension between the inferred sum of neutrino masses and the minimum value allowed by neutrino oscillation experiments, when assuming an underlying $\Lambda$CDM model of…

In the conventional framework for cosmological dynamics the scale factor $a(t)$ is assumed to obey the `background' Friedmann equation for a perfectly homogeneous universe while particles move according to equations of motions driven by the…

With the era of precision cosmology upon us, and upcoming surveys expected to further improve the precision of our observations below the percent level, ensuring the accuracy of our theoretical cosmological model is of the utmost…

Nested error regression models are useful tools for analysis of grouped data, especially in the case of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in…

(abridged) We used a suite of numerical cosmological simulations in order to investigate the effect of gas cooling and star formation on the large scale matter distribution. The simulations follow the formation of cosmic structures in five…

Recently H(z) data obtained from differential ages of galaxies have been proposed as a new geometrical probe of dark energy. In this paper we use those data, combined with other background tests (CMB shift and SNIa data), to constrain a set…

Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Developments in estimation procedures have helped EAMs become…

The Bayesian approach to inverse problems typically relies on posterior sampling approaches, such as Markov chain Monte Carlo, for which the generation of each sample requires one or more evaluations of the parameter-to-observable map or…

Bayesian model averaging is a procedure to obtain parameter constraints that account for the uncertainty about the correct cosmological model. We use recent cosmological observations and Bayesian model averaging to derive tight limits on…

The paper discusses back reaction effects in cosmology, (\'{a} la Buchert et. al.), induced by noncommutative geometry effects in fluid. We have used generalizations of an action formulation of noncommutative fluid model, proposed earlier…

We provide a number-conserving approach to the backreaction problem of small quantum fluctuations onto a classical background for the exactly soluble dynamical evolution of a Bose-Einstein condensate, experimentally realizable in the…

The precise cosmological origin of globular clusters remains uncertain, a situation hampered by the struggle of observational approaches in conclusively identifying the presence, or not, of dark matter in these systems. In this paper, we…

In order to test if there is energy transfer between dark energy and dark matter, we investigate cosmological constraints on two forms of nontrivial interaction between the dark matter sector and the sector responsible for the acceleration…

We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in statistics and machine learning, including Multinomial-Dirichlet…

For many physical systems which can be approximated by a classical background field plus small (linearized) quantum fluctuations, a fundamental question concerns the correct description of the backreaction of the quantum fluctuations onto…

Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not…