Results 11 to 20 of about 157,640 (328)
Bayesian Hierarchical Modelling for Tailoring Metric Thresholds [PDF]
Proceedings of the 15th International Conference on Mining Software Repositories, 2018 Software is highly contextual. While there are cross-cutting `global' lessons, individual software projects exhibit many `local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous.
Bettenburg N. +4 more
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Bayesian hierarchical modeling: an introduction and reassessment. [PDF]
Behav Res Methods, 2022 With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account of inter- and intraindividual variability and are particularly suited for the evaluation of repeated-measures designs. Here, we provide guidance for model
Veenman M, Stefan AM, Haaf JM.
europepmc +5 more sources
A Bayesian Hierarchical Model for Criminal Investigations [PDF]
Bayesian Analysis, 2021 Potential violent criminals will often need to go through a sequence of preparatory steps before they can execute their plans. During this escalation process police have the opportunity to evaluate the threat posed by such people through what they know, observe and learn from intelligence reports about their activities.
Bunnin, F. O., Smith, J. Q.
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Hierarchical Bayesian Modeling of Pharmacophores in Bioinformatics [PDF]
Biometrics, 2010 One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application,
Mardia, Kanti V. +4 more
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Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction
Journal of Applied Mathematics, 2021 Over the past few years, hierarchical Bayesian models have been extensively used for modeling the joint spatial and temporal dependence of big spatio-temporal data which commonly involves a large number of missing observations.
Esam Mahdi +3 more
doaj +1 more source
Alexandria Engineering Journal, 2023 The hierarchical models have not only a major concern with developing computational schemes but also assist in inferring the multi-parameter problems.
Azeem Iqbal +2 more
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A Hierarchical Bayesian Model for Crowd Emotions [PDF]
Frontiers in Computational Neuroscience, 2016 Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions.
Oscar J. Urizar +5 more
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Frontiers in Environmental Science, 2021 Large compilations of heterogeneous environmental observations are increasingly available as public databases, allowing researchers to test hypotheses across datasets.
Gregory L. Britten +13 more
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A Hierarchical Bayesian Model of Adaptive Teaching
Cognitive Science, 2022 Abstract How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy.
Alicia M. Chen +4 more
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Technical note: Modeling spatial fields of extreme precipitation – a hierarchical Bayesian approach [PDF]
Hydrology and Earth System Sciences, 2022 We introduce a hierarchical Bayesian model for the spatial distribution of rainfall corresponding to an extreme event of a specified duration that could be used with regional hydrologic models to perform a regional hydrologic risk analysis.
B. Rahill-Marier, N. Devineni, U. Lall
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