Results 11 to 20 of about 50,616 (261)

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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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, Angelika Marlene Stefan, Julia M. Haaf   +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, Andrew Palacci, Natalia Vélez, Robert D. Hawkins, Samuel J. Gershman   +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, Nyirongo, VB, Fallaize, CJ, Barber, S, Jackson, RM   +4 more
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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, Pesquet, Jean-Christophe, Tourneret, Jean-Yves, Ciuciu, Philippe, Benazza-Benyahia, Amel   +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, Mirza Sulman Baig, Emilia I. Barakova, Carlo S. Regazzoni, Lucio Marcenaro, Matthias Rauterberg   +5 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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Bayesian hierarchical model for bias-correcting climate models [PDF]

Geoscientific Model Development
Climate 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, J. Carter, E. A. Chacón-Montalván, A. Leeson, A. Leeson   +4 more
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Bayesian hierarchical models and prior elicitation for fitting psychometric functions

Frontiers in Computational Neuroscience, 2023
Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods.
Maura Mezzetti, Colleen P. Ryan, Colleen P. Ryan, Priscilla Balestrucci, Francesco Lacquaniti, Francesco Lacquaniti, Alessandro Moscatelli, Alessandro Moscatelli   +7 more
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Modelling unexpected failures with a hierarchical Bayesian model [PDF]

2017 2nd International Conference on System Reliability and Safety (ICSRS), 2017
Systems, especially those in the design and development phase, frequently suffer from unexpected failures, which are caused by insufficient knowledge of the system failure processes. In this paper, we develop a hierarchical Bayesian reliability model that account for unexpected failures.
Zeng, Zhiguo, Zio, Enrico
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