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We discuss the use of the Bayesian evidence ratio, or Bayes factor, for model selection in astronomy. We treat the evidence ratio as a statistic and investigate its distribution over an ensemble of experiments, considering both simple…

Instrumentation and Methods for Astrophysics · Physics 2015-05-27 C. R. Jenkins , J. A. Peacock

We provide a new interpretation for the Bayes factor combination used in the Dark Energy Survey (DES) first year analysis to quantify the tension between the DES and Planck datasets. The ratio quantifies a Bayesian confidence in our ability…

Cosmology and Nongalactic Astrophysics · Physics 2019-11-26 Will Handley , Pablo Lemos

The ratio of Bayesian evidences is a popular tool in cosmology to compare different models. There are however several issues with this method: Bayes' ratio depends on the prior even in the limit of non-informative priors, and Jeffrey's…

Cosmology and Nongalactic Astrophysics · Physics 2024-12-16 Luca Amendola , Vrund Patel , Ziad Sakr , Elena Sellentin , Kevin Wolz

We introduce a new conservative test for quantifying the consistency of two or more datasets. The test is based on the Bayesian answer to the question, ``How much more probable is it that all my data were generated from the same model…

Astrophysics · Physics 2008-11-26 Phil Marshall , Nutan Rajguru , Anze Slosar

Summary statistics of the likelihood, such as the Bayesian evidence, offer a principled way of comparing models and assessing tension between, or within, the results of physical experiments. Noisy realisations of the data induce scatter in…

Cosmology and Nongalactic Astrophysics · Physics 2021-04-30 B. Joachimi , F. Köhlinger , W. Handley , P. Lemos

Bayesian evidence ratios give a very attractive way of comparing models, and being able to quote the odds on a particular model seems a very clear motivation for making a choice. Jeffreys' scale of evidence is often used in the…

Instrumentation and Methods for Astrophysics · Physics 2020-09-07 Charles Jenkins

We use mock galaxy survey simulations designed to resemble the Dark Energy Survey Year 1 (DES Y1) data to validate and inform cosmological parameter estimation. When similar analysis tools are applied to both simulations and real survey…

We look at the distribution of the Bayesian evidence for mock realizations of supernova and baryon acoustic oscillation data. The ratios of Bayesian evidences of different models are often used to perform model selection. The significance…

Cosmology and Nongalactic Astrophysics · Physics 2022-07-20 Ryan E. Keeley , Arman Shafieloo

Beyond-$\Lambda$CDM physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analyzed assuming $\Lambda$CDM. We present an application of internal consistency tests to measurements from the Dark…

Tensions between cosmological measurements by different surveys or probes have always been important --- and are presently much discussed --- as they may lead to evidence of new physics. Several tests have been devised to probe the…

Cosmology and Nongalactic Astrophysics · Physics 2019-01-30 Saroj Adhikari , Dragan Huterer

Future weak lensing surveys will directly probe the density fluctuation in the universe. Recent studies have shown how the statistics of the weak lensing convergence field is related to the statistics of collapsed objects. Extending earlier…

Astrophysics · Physics 2008-11-26 Dipak Munshi

When extracting the weak lensing shear signal, one may employ either locally normalized or globally normalized shear estimators. The former is the standard approach when estimating cluster masses, while the latter is the more common method…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-19 Eduardo Rozo , Hao-Yi Wu , Fabian Schmidt

Combined survey analyses of galaxy clustering and weak gravitational lensing (3x2-pt studies) will allow new and accurate tests of the standard cosmological model. However, careful validation is necessary to ensure that these cosmological…

Both the Bayes factor and the relative belief ratio satisfy the principle of evidence and so can be seen to be valid measures of statistical evidence. Certainly Bayes factors are regularly employed. The question then is: which of these…

Statistics Theory · Mathematics 2024-02-28 Luai Al-Labadi , Ayman Alzaatreh , Michael Evans

Empirical fixation densities, spatial distributions estimated from human eye-tracking data, are foundational to saliency benchmarking. They directly shape benchmark conclusions, leaderboard rankings, failure case analyses, and scientific…

Computer Vision and Pattern Recognition · Computer Science 2026-05-06 Susmit Agrawal , Jannis Hollman , Matthias Kümmerer

Assessing the consistency of parameter constraints derived from different cosmological probes is an important way to test the validity of the underlying cosmological model. In an earlier work [Nicola et al., 2017], we computed constraints…

Cosmology and Nongalactic Astrophysics · Physics 2017-11-01 Andrina Nicola , Adam Amara , Alexandre Refregier

When fits of the same physical model to two different datasets disagree, we call this tension. Several apparent tensions in cosmology have occupied researchers in recent years, and a number of different metrics have been proposed to…

Cosmology and Nongalactic Astrophysics · Physics 2026-02-25 Harry T. J. Bevins , William J. Handley , Thomas Gessey-Jones

In cosmological analyses it is common to combine different types of measurement from the same survey. In this paper we use simulated DES Y3 and LSST Y1 data to explore differences in sensitivity to intrinsic alignments (IA) between cosmic…

Cosmology and Nongalactic Astrophysics · Physics 2024-09-12 S. Samuroff , A. Campos , A. Porredon , J. Blazek

Weak gravitational lensing has been used extensively in the past decade to constrain the masses of galaxy clusters, and is the most promising observational technique for providing the mass calibration necessary for precision cosmology with…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-14 Rachel Mandelbaum , Uros Seljak , Tobias Baldauf , Robert E. Smith

Deep clustering methods improve the performance of clustering tasks by jointly optimizing deep representation learning and clustering. While numerous deep clustering algorithms have been proposed, most of them rely on artificially…

Machine Learning · Computer Science 2024-01-30 Zhanwen Cheng , Feijiang Li , Jieting Wang , Yuhua Qian
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