Results 21 to 30 of about 7,662,414 (297)
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, KV +4 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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Hierarchical Bayesian modeling of interoceptive psychophysics. [PDF]
Behav Res MethodsAbstract Interoception, the capacity to sense, perceive, and metacognitively appraise viscerosensory and homeostatic signals, is a growing focus in psychology and psychiatry. Adaptive psychophysical tasks now allow quantification of perceptual sensitivity, bias, and precision in cardiac and respiratory domains.
Courtin AS +4 more
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Sensors, 2020 Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data.
Mingming Song +3 more
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Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data. [PDF]
PLoS ONE, 2015 It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a ...
Ting Tian +3 more
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Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior
Stats, 2022 We discuss a Bayesian hierarchical copula model for clusters of financial time series. A similar approach has been developed in recent paper. However, the prior distributions proposed there do not always provide a proper posterior. In order to circumvent
Paolo Onorati, Brunero Liseo
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Hierarchical Bayesian Models for Multiple Count Data
Austrian Journal of Statistics, 2016 The aim of this paper is to develop a model for analyzing multiple response models for count data and that may take into account complex correlation structures. The model is specified hierarchically in several layers and can be used for sparse data as it
Radu Tunaru
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Bayesian methods for hierarchical distance sampling models [PDF]
, 2014 Cornelia S. Oedekoven was supported by a studentship jointly funded by the University of St Andrews and EPSRC (EPSRC grant EP/C522702/1), through the National Centre for Statistical Ecology.The few distance sampling studies that use Bayesian methods ...
Oedekoven, Cornelia Sabrina +14 more
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Bayesian hierarchical model for bias-correcting climate models [PDF]
Geoscientific Model DevelopmentClimate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently ...
J. Carter +4 more
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Joint segmentation of wind speed and direction using a hierarchical model [PDF]
, 2007 The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series.
Dobigeon, Nicolas, Tourneret, Jean-Yves
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