Results 11 to 20 of about 7,662,414 (297)
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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Hierarchical Graphical Bayesian Models in Psychology
Revista Colombiana de Estadística, 2014 The improvement of graphical methods in psychological research can promote their use and a better comprehension of their expressive power. The application of hierarchical Bayesian graphical models has recently become more frequent in psychological ...
GUILLERMO CAMPITELLI, GUILLERMO MACBETH
doaj +5 more sources
, 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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Hierarchic Bayesian models for kernel learning [PDF]
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes ...
Mark A. Girolami, Simon Rogers
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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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Bayesian hierarchical models for linear networks. [PDF]
J Appl Stat, 2022 The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process.
Al-Kaabawi Z, Wei Y, Moyeed R.
europepmc +4 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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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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