Related papers: Predicting extragalactic distance errors using Bay…
New estimates of the distances of 36 nearby galaxies are presented based on accurate distances of galactic Cepheids obtained by Gieren, Fouque and Gomez (1998) from the geometrical Barnes-Evans method. The concept of 'sosie' is applied to…
Several rapid parameter estimation methods have recently been advanced to deal with the computational challenges of the problem of Bayesian inference of the properties of compact binary sources detected in the upcoming science runs of the…
Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to…
21 st century astrophysicists are confronted with the herculean task of distilling the maximum scientific return from extremely expensive and complex space- or ground-based instrumental projects. This paper concentrates in the mining of the…
We investigate the feasibility of determining the pairwise velocity dispersion (PVD) for Lyman Break Galaxies (LBGs), and of using this quantity as a discriminator among theoretical models. We find that when the Two Point Correlation…
Especially when facing reliability data with limited information (e.g., a small number of failures), there are strong motivations for using Bayesian inference methods. These include the option to use information from physics-of-failure or…
We define a novel class of additive models, called Extended Latent Gaussian Models, that allow for a wide range of response distributions and flexible relationships between the additive predictor and mean response. The new class covers a…
This work presents a novel and effective method for fitting multidimensional ellipsoids to scattered data in the contamination of noise and outliers. We approach the problem as a Bayesian parameter estimate process and maximize the…
Powerful current and future cosmological constraints using high precision measurements of the large-scale structure of galaxies and its weak gravitational lensing effects rely on accurate characterization of the redshift distributions of…
Observational astrophysics consists of making inferences about the Universe by comparing data and models. The credible intervals placed on model parameters are often as important as the maximum a posteriori probability values, as the…
Spectrophotometric distances to stars observed by large spectroscopic surveys offer a crucial complement to parallax distances that remain very important also after the future Gaia data releases. Here we present a probabilistic approach to…
Increasingly complex applications involve large datasets in combination with non-linear and high dimensional mathematical models. In this context, statistical inference is a challenging issue that calls for pragmatic approaches that take…
We present an empirical method for estimating the underlying redshift distribution N(z) of galaxy photometric samples from photometric observables. The method does not rely on photometric redshift (photo-z) estimates for individual…
Filament finders are limited, among other things, by the abundance of spectroscopic redshift data. As there are proportionally more photometric redshift data than spectroscopic, we aim to use photometric data to improve and expand the areas…
Analysis of the peculiar velocities of galaxies should take account of the uncertainties in both redshifts and distances. We show how this can be done by a numerical application of the action principle. The method is applied to an improved…
We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights.…
We developed a code that estimates distances to stars using measured spectroscopic and photometric quantities. We employ a Bayesian approach to build the probability distribution function over stellar evolutionary models given these data,…
Noisy distance estimates associated with photometric rather than spectroscopic redshifts lead to a mis-estimate of the luminosities, and produce a correlated mis-estimate of the sizes. We consider a sample of early-type galaxies from the…
We present a catalog of 5598 ultra-diffuse galaxy (UDG) candidates with effective radius $r_e > 5.3$ arcsec distributed throughout the southern portion of the DESI Legacy Imaging Survey covering $\sim$ 15000 deg$^2$. The catalog is most…
Bayesian inference provides a principled probabilistic framework for quantifying uncertainty by updating beliefs based on prior knowledge and observed data through Bayes' theorem. In Bayesian deep learning, neural network weights are…