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Grand Canonical Ensembles of Sparse Networks and Bayesian Inference
Entropy, 2022 Maximum entropy network ensembles have been very successful in modelling sparse network topologies and in solving challenging inference problems. However the sparse maximum entropy network models proposed so far have fixed number of nodes and are ...
Ginestra Bianconi
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Bayesian hierarchical response time modelling—A tutorial
British Journal of Mathematical and Statistical Psychology, 2023 Abstract Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on
Christoph Koenig +2 more
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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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Frontiers in Psychology, 2016 Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modelling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design ...
Moritz eBoos +3 more
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IEEE Access, 2019 Degradation analysis methods are increasingly used in reliability assessment of long-life products. The small sample size problem is worthy of attention in the degradation analysis.
Junyu Guo +3 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.
Urizar, Oscar J +5 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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Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach
Remote Sensing, 2019 Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar
Mengxi Wang +4 more
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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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, 2013 Sub-millimetre dust emission provides information on the physics of interstellar clouds and dust. Noise can produce anticorrelation between the colour temperature T_C and the spectral index beta. This must be separated from the intrinsic beta(T) relation
Juvela, M. +3 more
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