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Improving randomness characterization through Bayesian model selection [PDF]
Scientific Reports, 2017 Random number generation plays an essential role in technology with important applications in areas ranging from cryptography to Monte Carlo methods, and other probabilistic algorithms. All such applications require high-quality sources of random numbers,
Rafael Díaz Hernández Rojas +6 more
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Bayesian model selection for spatial capture–recapture models [PDF]
Ecology and Evolution, 2019 A vast amount of ecological knowledge generated over the past two decades has hinged upon the ability of model selection methods to discriminate among various ecological hypotheses.
Soumen Dey +2 more
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A Bayesian model selection approach to mediation analysis. [PDF]
PLoS Genetics, 2022 Genetic studies often seek to establish a causal chain of events originating from genetic variation through to molecular and clinical phenotypes. When multiple phenotypes share a common genetic association, one phenotype may act as an intermediate for ...
Wesley L Crouse +4 more
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Bayesian model selection for multilevel models using integrated likelihoods. [PDF]
PLoS ONE, 2023 Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the
Tom Edinburgh, Ari Ercole, Stephen Eglen
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Application of referenced thermodynamic integration to Bayesian model selection. [PDF]
PLoS ONE, 2023 Evaluating normalising constants is important across a range of topics in statistical learning, notably Bayesian model selection. However, in many realistic problems this involves the integration of analytically intractable, high-dimensional ...
Iwona Hawryluk +4 more
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Bayesian model selection for complex dynamic systems [PDF]
Nature Communications, 2018 Systematic changes in stock market prices or in the migration behaviour of cancer cells may be hidden behind random fluctuations. Here, Mark et al. describe an empirical approach to identify when and how such real-world systems undergo systematic changes.
Christoph Mark +5 more
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Bayesian Model Selection With Network Based Diffusion Analysis [PDF]
Frontiers in Psychology, 2016 A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population.
Andrew eWhalen +1 more
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Detection of arterial wall abnormalities via Bayesian model selection [PDF]
Royal Society Open Science, 2019 Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models.
Karen Larson +4 more
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A sensorimotor paradigm for Bayesian model selection [PDF]
Frontiers in Human Neuroscience, 2012 Sensorimotor control is thought to rely on predictive internal models in order to cope efficientlywith uncertain environments. Recently, it has been shown that humans not only learn differentinternal models for different tasks, but that they also extract
Tim eGenewein +3 more
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Bayesian model selection for the Drosophila gap gene network [PDF]
BMC Bioinformatics, 2019 Background The gap gene system controls the early cascade of the segmentation pathway in Drosophila melanogaster as well as other insects. Owing to its tractability and key role in embryo patterning, this system has been the focus for both computational ...
Asif Zubair +3 more
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