Results 11 to 20 of about 7,749,372 (301)
A hierarchical Bayesian model for frame representation [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients.
Chaari, Lotfi +4 more
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Bayesian designs for hierarchical linear models [PDF]
Statistica Sinica, 2009 Summary: Two Bayesian optimal design criteria for hierarchical linear models are discussed: the \(\psi_\beta\) criterion for the estimation of individual-level parameters \(\beta\), and the \(\psi_\theta\) criterion for the estimation of hyperparameters \(\mathbf \theta\).
Liu, Qing +2 more
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, 2015 This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology ...
N. Thompson Hobbs, Mevin B. Hooten
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Hierarchical Bayesian models of delusion
Consciousness and Cognition, 2018 Researchers in the field of computational psychiatry have recently sought to model the formation and retention of delusions in terms of dysfunctions in a process of hierarchical Bayesian inference. I present a systematic review of such models and raise two challenges that have not received sufficient attention in the literature.
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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 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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Bayesian Hierarchical Modeling: An Introduction and Reassessment
Behavior Research 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
Myrthe Veenman +2 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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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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