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Top Papers in Bayesian model

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When are Bayesian model probabilities overconfident?

Bayesian model comparison is often based on the posterior distribution over
the set of compared models. This distribution is often observed to concentrate
on a single model even when other measures of

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clusterBMA: Bayesian model averaging for clustering

Various methods have been developed to combine inference across multiple sets
of results for unsupervised clustering, within the ensemble and consensus
clustering literature. The approach of reporting

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A Parsimonious Tour of Bayesian Model Uncertainty

Modern statistical software and machine learning libraries are enabling
semi-automated statistical inference. Within this context, it appears easier
and easier to try and fit many models to the data a

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Meta-Uncertainty in Bayesian Model Comparison

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 possibl

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Sequential Bayesian Model Selection of Regular Vine Copulas

Regular vine copulas can describe a wider array of dependency patterns than
the multivariate Gaussian copula or the multivariate Student's t copula. This
paper presents two contributions related to mo

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Dangers of Bayesian Model Averaging under Covariate Shift

Approximate Bayesian inference for neural networks is considered a robust
alternative to standard training, often providing good performance on
out-of-distribution data. However, Bayesian neural netwo

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A Bayesian Model of Cash Bail Decisions

The use of cash bail as a mechanism for detaining defendants pre-trial is an
often-criticized system that many have argued violates the presumption of
"innocent until proven guilty." Many studies have

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A subsampling approach for Bayesian model selection

It is common practice to use Laplace approximations to compute marginal
likelihoods in Bayesian versions of generalised linear models (GLM). Marginal
likelihoods combined with model priors are then us

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A Bayesian Model-Averaged Meta-Analysis of Clinical Evidence

Bayesian Model-Averaged Meta-Analysis in Medicine

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Bayesian discrimination of the panchromatic spectral energy distribution modelings of galaxies

Fitting the multi-wavelength spectral energy distributions (SEDs) of galaxies
is a widely used technique to extract information about the physical properties
of galaxies. However, a major difficulty l

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Escaping the curse of dimensionality in Bayesian model based clustering

In many applications, there is interest in clustering very high-dimensional
data. A common strategy is first stage dimensionality reduction followed by a
standard clustering algorithm, such as k-means

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Bayesian Model Selection Based on Proper Scoring Rules

Bayesian model selection with improper priors is not well-defined because of
the dependence of the marginal likelihood on the arbitrary scaling constants of
the within-model prior densities. We show h

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Bayesian Model Selection, the Marginal Likelihood, and Generalization

How do we compare between hypotheses that are entirely consistent with
observations? The marginal likelihood (aka Bayesian evidence), which represents
the probability of generating our observations fr

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What is a Bayesian Model?

Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy

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Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature

This paper presents a Bayesian model selection approach via Bayesian
quadrature and sensitivity analysis of the selection criterion for a
lithium-ion battery model. The Bayesian model evidence is adop

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Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization

Bayesian model reduction provides an efficient approach for comparing the
performance of all nested sub-models of a model, without re-evaluating any of
these sub-models. Until now, Bayesian model redu

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Bayesian model averaging with the integrated nested Laplace approximation

The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hie

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Bayesian Model Comparison with SEDs of 3D-HST galaxies

Detecting episodes of star formation using Bayesian model selection

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Federated Learning with Bayesian Model Ensemble Aggregation

FedDistill: Making Bayesian Model Ensemble Applicable to Federated Learning

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Bayesian model-based outlier detection in network meta-analysis

In a network meta-analysis, some of the collected studies may deviate
markedly from the others, for example having very unusual effect sizes. These
deviating studies can be regarded as outlying with r

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